Difference between revisions of "Biocluster Applications"
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|[http://www-inf.enst.fr/~demaille/a2ps/ a2ps] | |[http://www-inf.enst.fr/~demaille/a2ps/ a2ps] | ||
− | |4.14-IGB-gcc-4.9.4 | + | |4.14-IGB-gcc-4.9.4<br>4.14-IGB-gcc-8.2.0 |
|a2ps-4.14: Formats an ascii file for printing on a postscript printer | |a2ps-4.14: Formats an ascii file for printing on a postscript printer | ||
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|1.2.64 | |1.2.64 | ||
|Random forests methodologies for ABC model choice and ABC Bayesian parameter inference ( | |Random forests methodologies for ABC model choice and ABC Bayesian parameter inference ( | ||
+ | |- | ||
+ | |[https://abseil.io/ Abseil] | ||
+ | |20230125.2-IGB-gcc-8.2.0<br>20230125.3-IGB-gcc-8.2.0 | ||
+ | |Abseil is an open-source collection of C++ library code designed to augment theC++ standard library. The Abseil library code is collected from Google's ownC++ code base, has been extensively tested and used in production, and is thesame code we depend on in our daily coding lives. | ||
|- | |- | ||
|[http://www.bcgsc.ca/platform/bioinfo/software/abyss ABySS] | |[http://www.bcgsc.ca/platform/bioinfo/software/abyss ABySS] | ||
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|0.4.2 | |0.4.2 | ||
|alevin-fry is a suite of tools for the rapid, accurate and memory-frugal processing single-cell and single-nucleus sequencing data. | |alevin-fry is a suite of tools for the rapid, accurate and memory-frugal processing single-cell and single-nucleus sequencing data. | ||
+ | |- | ||
+ | |[https://github.com/biocompibens/ALFA alfa] | ||
+ | |1.1.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |ALFA provides a global overview of features distribution composing NGS dataset(s). | ||
|- | |- | ||
|[https://github.com/baoe/AlignGraph AlignGraph] | |[https://github.com/baoe/AlignGraph AlignGraph] | ||
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|AlignGraph is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism. | |AlignGraph is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism. | ||
|- | |- | ||
− | |[https://github.com/ | + | |[https://github.com/dialvarezs/alphafold alphafold] |
− | |2. | + | |2.1.2<br>2.3.1<br>2.3.2 |
|This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. | |This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. | ||
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|[https://www.continuum.io/anaconda-overview Anaconda3] | |[https://www.continuum.io/anaconda-overview Anaconda3] | ||
− | |2019.10<br>5.0.1<br>5.1.0 | + | |2019.10<br>2022.05<br>2023.09<br>5.0.1<br>5.1.0 |
|Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. | |Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. | ||
|- | |- | ||
|[https://github.com/ANGSD/angsd/ ANGSD] | |[https://github.com/ANGSD/angsd/ ANGSD] | ||
− | |0.933-IGB-gcc-4.9.4 | + | |0.933-IGB-gcc-4.9.4<br>0.941-IGB-gcc-8.2.0 |
|ANGSD is a software for analyzing next generation sequencing data. The software can handle a number of different input types from mapped reads to imputed genotype probabilities. | |ANGSD is a software for analyzing next generation sequencing data. The software can handle a number of different input types from mapped reads to imputed genotype probabilities. | ||
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|[http://ant.apache.org/ ant] | |[http://ant.apache.org/ ant] | ||
− | |1.10.1-Java-1.8.0_121<br>1.10.1-Java-1.8.0_152<br>1.10.10-Java-15.0.1<br>1.10.9-Java-1.8.0_201 | + | |1.10.1-Java-1.8.0_121<br>1.10.1-Java-1.8.0_152<br>1.10.10-Java-15.0.1<br>1.10.13-Java-15.0.1<br>1.10.9-Java-1.8.0_201 |
|Apache Ant is a Java library and command-line tool whose mission is to drive processes described in build files as targets and extension points dependent upon each other. The main known usage of Ant is the build of Java applications. | |Apache Ant is a Java library and command-line tool whose mission is to drive processes described in build files as targets and extension points dependent upon each other. The main known usage of Ant is the build of Java applications. | ||
|- | |- | ||
|[https://bitbucket.org/antismash/antismash/ antismash] | |[https://bitbucket.org/antismash/antismash/ antismash] | ||
− | |4.1.0<br>5.1.2 | + | |4.1.0<br>5.1.2<br>6.1.0<br>7.1.0-0 |
|antiSMASH allows the rapid genome-wide identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genomes. It integrates and cross-links with a large number of in silico secondary metabolite analysis tools that have been published earlier. | |antiSMASH allows the rapid genome-wide identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genomes. It integrates and cross-links with a large number of in silico secondary metabolite analysis tools that have been published earlier. | ||
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|20200510-IGB-gcc-8.2.0 | |20200510-IGB-gcc-8.2.0 | ||
|A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm | |A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm | ||
+ | |- | ||
+ | |[http://www.arb-home.de ARB] | ||
+ | |6.0.6 | ||
+ | |The ARB software is a graphically oriented package comprising various tools for sequence database handling and data analysis. A central database of processed (aligned) sequences and any type of additional data linked to the respective sequence entries is structured according to phylogeny or other user defined criteria | ||
|- | |- | ||
|[https://github.com/mgbellemare/Arcade-Learning-Environment ArcadeLearningEnvironment] | |[https://github.com/mgbellemare/Arcade-Learning-Environment ArcadeLearningEnvironment] | ||
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|argtable | |argtable | ||
| | | | ||
− | |Argtable is an ANSI C library for parsing GNU style command line options with a minimum of fuss. | + | |Argtable is an ANSI C library for parsing GNU style command line options with a minimum of fuss. |
+ | |- | ||
+ | |[https://aria2.github.io aria2] | ||
+ | |1.36.0-IGB-gcc-8.2.0<br>1.37.0-IGB-gcc-8.2.0 | ||
+ | |aria2 is a lightweight multi-protocol & multi-source command-line download utility. | ||
|- | |- | ||
|[http://cmpg.unibe.ch/software/arlequin35/Arlequin35.html Arlequin] | |[http://cmpg.unibe.ch/software/arlequin35/Arlequin35.html Arlequin] | ||
|3.5 | |3.5 | ||
|An Integrated Software for Population Genetics Data Analysis | |An Integrated Software for Population Genetics Data Analysis | ||
+ | |- | ||
+ | |[https://github.com/TheFraserLab/ASEr ASEr] | ||
+ | |0.2-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |Get ASE counts from BAMs or raw fastq data | ||
|- | |- | ||
|[http://asperasoft.com/ aspera] | |[http://asperasoft.com/ aspera] | ||
− | |3.7.6 | + | |3.7.6<br>4.2.7.445 |
|Aspera’s unwavering mission is to create the next-generation software technologies that move the world’s data at maximum speed, regardless of file size, transfer distance and network conditions. | |Aspera’s unwavering mission is to create the next-generation software technologies that move the world’s data at maximum speed, regardless of file size, transfer distance and network conditions. | ||
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| | | | ||
|This bundle collect the standard GNU build tools: Autoconf, Automake and libtool | |This bundle collect the standard GNU build tools: Autoconf, Automake and libtool | ||
+ | |- | ||
+ | |[https://github.com/mennthor/awkde awkde] | ||
+ | |20220617-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |This uses the awesome pybind11 package which makes creating C++ bindings super convenient. Only the evaluation is written in a small C++ snippet to speed it up, the rest is a pure python implementation. | ||
|- | |- | ||
|[https://aws.amazon.com/cli/ awscli] | |[https://aws.amazon.com/cli/ awscli] | ||
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|0.9-IGB-gcc-4.9.4 | |0.9-IGB-gcc-4.9.4 | ||
|Barrnap predicts the location of ribosomal RNA genes in genomes. | |Barrnap predicts the location of ribosomal RNA genes in genomes. | ||
+ | |- | ||
+ | |[https://github.com/zanglab/bart2 bart2] | ||
+ | |20240302-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |BART (Binding Analysis for Regulation of Transcription) is a bioinformatics tool for predicting functional transcriptional regulators (TRs) that bind at genomic cis-regulatory regions to regulate gene expression in the human or mouse genomes, taking a query gene set, a ChIP-seq dataset or a scored genomic region set as input. | ||
+ | |- | ||
+ | |[https://developer.basespace.illumina.com/docs/content/documentation/cli/cli-overview basespace-cli] | ||
+ | |1.5.1 | ||
+ | |You can work with your BaseSpace Sequence Hub data using the command line interface (CLI). The BaseSpace Sequence Hub CLI supports scripting and programmatic access to BaseSpace Sequence Hub for automation, bulk operations, and other routine functions. It can be used independently or in conjunction with BaseMount. | ||
|- | |- | ||
|[https://github.com/PacificBiosciences/bax2bam bax2bam] | |[https://github.com/PacificBiosciences/bax2bam bax2bam] | ||
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|[https://jgi.doe.gov/data-and-tools/bbtools/ BBMap] | |[https://jgi.doe.gov/data-and-tools/bbtools/ BBMap] | ||
− | |38.36-Java-1.8.0_152<br>38.94- | + | |38.36-Java-1.8.0_152<br>38.94-Java-1.8.0_201 |
|BBMap short read aligner, and other bioinformatic tools. | |BBMap short read aligner, and other bioinformatic tools. | ||
|- | |- | ||
|[http://www.htslib.org/ BCFtools] | |[http://www.htslib.org/ BCFtools] | ||
− | |1.12-IGB-gcc-8.2.0<br>1.4-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.7-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4 | + | |1.12-IGB-gcc-8.2.0<br>1.17-IGB-gcc-8.2.0<br>1.4-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.7-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4 |
|BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF | |BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF | ||
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|[https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html bcl2fastq2] | |[https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html bcl2fastq2] | ||
− | |2.20-IGB-gcc-8.2.0 | + | |2.20<br>2.20-IGB-gcc-8.2.0 |
|The bcl2fastq2 Conversion Software v2.20.0 can be used to convert BCL files from MiniSeq, MiSeq, NextSeq, HiSeq, and NovaSeq sequening systems. For conversion of data generated on Illumina sequencing systems using versions of RTA earlier than RTA 1.18.54, use bcl2fastq v1.8.4. | |The bcl2fastq2 Conversion Software v2.20.0 can be used to convert BCL files from MiniSeq, MiSeq, NextSeq, HiSeq, and NovaSeq sequening systems. For conversion of data generated on Illumina sequencing systems using versions of RTA earlier than RTA 1.18.54, use bcl2fastq v1.8.4. | ||
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|03Jul18.40b-Java-1.8.0_152<br>5.1-Java-1.8.0_152 | |03Jul18.40b-Java-1.8.0_152<br>5.1-Java-1.8.0_152 | ||
|Beagle is a software package for phasing genotypes and for imputing ungenotyped markers. Version 5.0 has new, fast algorithms for genotype phasing and imputation. | |Beagle is a software package for phasing genotypes and for imputing ungenotyped markers. Version 5.0 has new, fast algorithms for genotype phasing and imputation. | ||
+ | |- | ||
+ | |[https://github.com/beagle-dev/beagle-lib beagle-lib] | ||
+ | |4.0.0-IGB-gcc-8.2.0 | ||
+ | |BEAGLE is a high-performance library that can perform the core calculations at the heart of most Bayesian and Maximum Likelihood phylogenetics packages. It can make use of highly-parallel processors such as those in graphics cards (GPUs) found in many PCs. | ||
+ | |- | ||
+ | |[https://www.beast2.org/ BEAST2] | ||
+ | |2.7.5-IGB-gcc-8.2.0 | ||
+ | |BEAST 2 is a cross-platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies using strict or relaxed molecular clock models. | ||
+ | |- | ||
+ | |[https://www.beast2.org/ beast2] | ||
+ | |2.6.7-Java-1.8.0_201 | ||
+ | |BEAST 2 is a cross-platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies using strict or relaxed molecular clock models. | ||
|- | |- | ||
|[https://bedops.readthedocs.io/en/latest/ bedops] | |[https://bedops.readthedocs.io/en/latest/ bedops] | ||
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|2.21.0-IGB-gcc-4.9.4<br>2.26.0-IGB-gcc-4.9.4<br>2.28.0-IGB-gcc-8.2.0 | |2.21.0-IGB-gcc-4.9.4<br>2.26.0-IGB-gcc-4.9.4<br>2.28.0-IGB-gcc-8.2.0 | ||
|The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. | |The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. | ||
+ | |- | ||
+ | |[https://github.com/pmelsted/bifrost Bifrost] | ||
+ | |1.0.4-IGB-gcc-8.2.0 | ||
+ | |Parallel construction, indexing and querying of colored and compacted de Bruijn graphs | ||
|- | |- | ||
|[https://github.com/HAugustijn/BiG-MAP2 big-map2-analyse] | |[https://github.com/HAugustijn/BiG-MAP2 big-map2-analyse] | ||
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|[https://git.wageningenur.nl/medema-group/BiG-SCAPE BiG-SCAPE] | |[https://git.wageningenur.nl/medema-group/BiG-SCAPE BiG-SCAPE] | ||
− | |1.0.1-IGB-gcc-4.9.4-Python-3.6.1<br> | + | |1.0.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.1.5-IGB-gcc-8.2.0-Python-3.7.2 |
|BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) is a software package, written in Python, that constructs sequence similarity networks of Biosynthetic Gene Clusters (BGCs) and groups them into Gene Cluster Families (GCFs) | |BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) is a software package, written in Python, that constructs sequence similarity networks of Biosynthetic Gene Clusters (BGCs) and groups them into Gene Cluster Families (GCFs) | ||
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|[http://www.biopython.org Biopython] | |[http://www.biopython.org Biopython] | ||
− | |1.68-IGB-gcc-4.9.4-Python-2.7.13<br>1.68-IGB-gcc-4.9.4-Python-3.6.1<br>1.76-IGB-gcc-4.9.4-Python-3.6.1<br>1.76-IGB-gcc-8.2.0-Python-3.7.2<br>1.79-IGB-gcc-8.2.0-Python-3.7.2 | + | |1.68-IGB-gcc-4.9.4-Python-2.7.13<br>1.68-IGB-gcc-4.9.4-Python-3.6.1<br>1.76-IGB-gcc-4.9.4-Python-3.6.1<br>1.76-IGB-gcc-8.2.0-Python-3.7.2<br>1.79-IGB-gcc-8.2.0-Python-3.7.2<br>1.83-IGB-gcc-8.2.0-Python-3.10.1 |
|Biopython is a set of freely available tools for biological computation writtenin Python by an international team of developers. It is a distributed collaborative effort todevelop Python libraries and applications which address the needs of current and future work inbioinformatics. | |Biopython is a set of freely available tools for biological computation writtenin Python by an international team of developers. It is a distributed collaborative effort todevelop Python libraries and applications which address the needs of current and future work inbioinformatics. | ||
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|[http://blast.ncbi.nlm.nih.gov/ BLAST+] | |[http://blast.ncbi.nlm.nih.gov/ BLAST+] | ||
− | |2.10.1-IGB-gcc-8.2.0<br>2.2.31-IGB-gcc-4.9.4<br>2.6.0-IGB-gcc-4.9.4<br>2.7.1-IGB-gcc-4.9.4<br>2.9.0-IGB-gcc-4.9.4 | + | |2.10.1-IGB-gcc-8.2.0<br>2.13.0-IGB-gcc-8.2.0<br>2.2.31-IGB-gcc-4.9.4<br>2.6.0-IGB-gcc-4.9.4<br>2.7.1-IGB-gcc-4.9.4<br>2.9.0-IGB-gcc-4.9.4 |
|Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. | |Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. | ||
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|3.5-IGB-gcc-4.9.4 | |3.5-IGB-gcc-4.9.4 | ||
|BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 25 bases or more. | |BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 25 bases or more. | ||
− | |||
− | |||
− | |||
− | |||
|- | |- | ||
|[https://github.com/DRL/blobtools blobtools] | |[https://github.com/DRL/blobtools blobtools] | ||
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|[https://github.com/blobtoolkit/blobtools2/archive/refs/tags blobtools2] | |[https://github.com/blobtoolkit/blobtools2/archive/refs/tags blobtools2] | ||
− | |2.6.1-IGB-gcc-4.9.4-Python-3.6.1 | + | |2.6.1-IGB-gcc-4.9.4-Python-3.6.1<br>2.6.4-IGB-gcc-8.2.0-Python-3.7.2 |
|Application for the visualisation of (draft) genome assemblies using TAGC (Taxon-annotated Gc-Coverage) plots | |Application for the visualisation of (draft) genome assemblies using TAGC (Taxon-annotated Gc-Coverage) plots | ||
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|[http://bowtie-bio.sourceforge.net/bowtie2/index.shtml Bowtie2] | |[http://bowtie-bio.sourceforge.net/bowtie2/index.shtml Bowtie2] | ||
− | |2.1.0-IGB-gcc-4.9.4<br>2.3.1-IGB-gcc-4.9.4<br>2.3.2-IGB-gcc-4.9.4<br>2.3.5.1-IGB-gcc-4.9.4<br>2.4.1-IGB-gcc-8.2.0<br>2.4.2-IGB-gcc-8.2.0 | + | |2.1.0-IGB-gcc-4.9.4<br>2.3.1-IGB-gcc-4.9.4<br>2.3.2-IGB-gcc-4.9.4<br>2.3.5.1-IGB-gcc-4.9.4<br>2.4.1-IGB-gcc-8.2.0<br>2.4.2-IGB-gcc-8.2.0<br>2.4.5-IGB-gcc-8.2.0<br>2.5.3-IGB-gcc-8.2.0 |
|Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes. | |Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes. | ||
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|[https://github.com/Gaius-Augustus/BRAKER BRAKER] | |[https://github.com/Gaius-Augustus/BRAKER BRAKER] | ||
− | |2.1.2-IGB-gcc-4.9.4<br>2.1.5-IGB-gcc-4.9.4<br>2.1.5-IGB-gcc-8.2.0<br>2.1.6-IGB-gcc-8.2.0 | + | |2.1.2-IGB-gcc-4.9.4<br>2.1.5-IGB-gcc-4.9.4<br>2.1.5-IGB-gcc-8.2.0<br>2.1.6-IGB-gcc-8.2.0<br>3.0.3-IGB-gcc-8.2.0 |
|BRAKER2 is an extension of BRAKER1 which allows for fully automated training of the gene prediction tools GeneMark-EX and AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction. | |BRAKER2 is an extension of BRAKER1 which allows for fully automated training of the gene prediction tools GeneMark-EX and AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction. | ||
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|[https://github.com/barricklab/breseq breseq] | |[https://github.com/barricklab/breseq breseq] | ||
− | |0.31.0-IGB-gcc-4.9.4 | + | |0.31.0-IGB-gcc-4.9.4<br>0.36.1-IGB-gcc-8.2.0<br>0.37.0-IGB-gcc-8.2.0 |
|is a computational pipeline for the analysis of short-read re-sequencing data (e.g. Illumina, 454, IonTorrent, etc.). It uses reference-based alignment approaches to predict mutations in a sample relative to an already sequenced genome. | |is a computational pipeline for the analysis of short-read re-sequencing data (e.g. Illumina, 454, IonTorrent, etc.). It uses reference-based alignment approaches to predict mutations in a sample relative to an already sequenced genome. | ||
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|[http://busco.ezlab.org/ BUSCO] | |[http://busco.ezlab.org/ BUSCO] | ||
− | |3.0.1-IGB-gcc-4.9.4-Python-2.7.13<br>4.1.4-IGB-gcc-8.2.0-Python-3.7.2<br>5.1.2-IGB-gcc-8.2.0-Python-3.7.2 | + | |3.0.1-IGB-gcc-4.9.4-Python-2.7.13<br>4.1.4-IGB-gcc-8.2.0-Python-3.7.2<br>5.1.2-IGB-gcc-8.2.0-Python-3.7.2<br>5.3.2-IGB-gcc-8.2.0-Python-3.7.2<br>5.4.4-IGB-gcc-8.2.0-Python-3.7.2<br>5.5.0-IGB-gcc-8.2.0-Python-3.7.2 |
|Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (BUSCO) | |Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (BUSCO) | ||
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|1.0.6-IGB-gcc-4.9.4<br>1.0.6-IGB-gcc-8.2.0 | |1.0.6-IGB-gcc-4.9.4<br>1.0.6-IGB-gcc-8.2.0 | ||
|bzip2 is a freely available, patent free, high-quality data compressor. It typically compresses files to within 10% to 15% of the best available techniques (the PPM family of statistical compressors), whilst being around twice as fast at compression and six times faster at decompression. | |bzip2 is a freely available, patent free, high-quality data compressor. It typically compresses files to within 10% to 15% of the best available techniques (the PPM family of statistical compressors), whilst being around twice as fast at compression and six times faster at decompression. | ||
+ | |- | ||
+ | |c-ares | ||
+ | | | ||
+ | |c-ares is a C library for asynchronous DNS requests (including name resolves) | ||
|- | |- | ||
|[https://github.com/ComparativeGenomicsToolkit/cactus cactus] | |[https://github.com/ComparativeGenomicsToolkit/cactus cactus] | ||
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|- | |- | ||
|[https://github.com/marbl/canu Canu] | |[https://github.com/marbl/canu Canu] | ||
− | |1.4-IGB-gcc-4.9.4-Perl-5.24.1<br>1.5-IGB-gcc-4.9.4-Perl-5.24.1<br>1.6-IGB-gcc-4.9.4-Perl-5.24.1<br>1.7-IGB-gcc-4.9.4-Perl-5.24.1<br>1.7.1-IGB-gcc-4.9.4-Perl-5.24.1<br>1.8-IGB-gcc-4.9.4-Perl-5.24.1<br>1.9-IGB-gcc-8.2.0-Perl-5.28.1<br>2.0-IGB-gcc-8.2.0-Perl-5.28.1<br>2.1.1-IGB-gcc-8.2.0-Perl-5.28.1 | + | |1.4-IGB-gcc-4.9.4-Perl-5.24.1<br>1.5-IGB-gcc-4.9.4-Perl-5.24.1<br>1.6-IGB-gcc-4.9.4-Perl-5.24.1<br>1.7-IGB-gcc-4.9.4-Perl-5.24.1<br>1.7.1-IGB-gcc-4.9.4-Perl-5.24.1<br>1.8-IGB-gcc-4.9.4-Perl-5.24.1<br>1.9-IGB-gcc-8.2.0-Perl-5.28.1<br>2.0-IGB-gcc-8.2.0-Perl-5.28.1<br>2.1.1-IGB-gcc-8.2.0-Perl-5.28.1<br>2.2-IGB-gcc-8.2.0-Perl-5.28.1 |
|Canu is a fork of the Celera Assembler, designed for high-noise single-molecule sequencing (such as the PacBio RS II or Oxford Nanopore MinION). | |Canu is a fork of the Celera Assembler, designed for high-noise single-molecule sequencing (such as the PacBio RS II or Oxford Nanopore MinION). | ||
|- | |- | ||
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|- | |- | ||
|[https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger cellranger] | |[https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger cellranger] | ||
− | |2.1.0<br>2.1.1<br>3.0.0<br>3.0.1<br>3.1.0<br>4.0.0<br>5.0.0<br>6.0.1<br>6.0.2<br>6.1.1 | + | |2.1.0<br>2.1.1<br>3.0.0<br>3.0.1<br>3.1.0<br>4.0.0<br>5.0.0<br>6.0.1<br>6.0.2<br>6.1.1<br>7.0.0<br>7.0.1<br>7.1.0<br>7.2.0<br>8.0.0 |
|Cell Ranger is a set of analysis pipelines that process Chromium single cell 3 RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. | |Cell Ranger is a set of analysis pipelines that process Chromium single cell 3 RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. | ||
|- | |- | ||
|[https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/what-is-cell-ranger-arc cellranger-arc] | |[https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/what-is-cell-ranger-arc cellranger-arc] | ||
− | |1.0.0 | + | |1.0.0<br>2.0.1<br>2.0.2 |
|Cell Ranger ARC is a set of analysis pipelines that process Chromium Single Cell Multiome ATAC + Gene Expression sequencing data to generate a variety of analyses pertaining to gene expression, chromatin accessibility and their linkage. | |Cell Ranger ARC is a set of analysis pipelines that process Chromium Single Cell Multiome ATAC + Gene Expression sequencing data to generate a variety of analyses pertaining to gene expression, chromatin accessibility and their linkage. | ||
|- | |- | ||
|[https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/what-is-cell-ranger-atac cellranger-atac] | |[https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/what-is-cell-ranger-atac cellranger-atac] | ||
− | |1.1.0<br>1.2.0<br>2.0.0 | + | |1.1.0<br>1.2.0<br>2.0.0<br>2.1.0 |
|Cell Ranger ATAC is a set of analysis pipelines that process Chromium Single Cell ATAC data. | |Cell Ranger ATAC is a set of analysis pipelines that process Chromium Single Cell ATAC data. | ||
|- | |- | ||
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|- | |- | ||
|[https://ceres-solver.googlesource.com/ceres-solver ceres-solver] | |[https://ceres-solver.googlesource.com/ceres-solver ceres-solver] | ||
− | |1.14.0-IGB-gcc-4.9.4 | + | |1.14.0-IGB-gcc-4.9.4<br>1.14.0-IGB-gcc-8.2.0<br>2.0.0-IGB-gcc-8.2.0 |
− | |Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems | + | |Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. |
|- | |- | ||
|[http://ecogenomics.github.io/CheckM CheckM] | |[http://ecogenomics.github.io/CheckM CheckM] | ||
− | |1.0.7-IGB-gcc-4.9.4-Python-2.7.13<br>1.1.3-IGB-gcc-8.2.0-Python-3.7.2 | + | |1.0.7-IGB-gcc-4.9.4-Python-2.7.13<br>1.1.3-IGB-gcc-8.2.0-Python-3.7.2<br>1.1.9-IGB-gcc-8.2.0-Python-3.7.2 |
|CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. | |CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. | ||
+ | |- | ||
+ | |[https://github.com/chklovski/CheckM2 CheckM2] | ||
+ | |1.0.1 | ||
+ | |Unlike CheckM1, CheckM2 has universally trained machine learning models it applies regardless of taxonomic lineage to predict the completeness and contamination of genomic bins. | ||
|- | |- | ||
|[https://bitbucket.org/valenlab/chopchop chopchop] | |[https://bitbucket.org/valenlab/chopchop chopchop] | ||
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|- | |- | ||
|[http://sanger-pathogens.github.io/circlator/ Circlator] | |[http://sanger-pathogens.github.io/circlator/ Circlator] | ||
− | |1.5.1-IGB-gcc-4.9.4-Python-3.6.1 | + | |1.5.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.5.5-IGB-gcc-8.2.0-Python-3.7.2 |
|A tool to circularize genome assemblies. | |A tool to circularize genome assemblies. | ||
|- | |- | ||
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|2.0-IGB-gcc-4.9.4 | |2.0-IGB-gcc-4.9.4 | ||
|An integrated tool for tiling array, ChIP-seq, genome and cis-regulatory element analysis | |An integrated tool for tiling array, ChIP-seq, genome and cis-regulatory element analysis | ||
+ | |- | ||
+ | |[https://pypi.org/project/clang-format clang-format] | ||
+ | |15.0.7-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |Clang-Format is an LLVM-based code formatting tool | ||
+ | |- | ||
+ | |[http://ccb.jhu.edu/people/florea/research/CLASS2/ CLASS2] | ||
+ | |2.1.7-IGB-gcc-8.2.0 | ||
+ | |CLASS2 is a fast and accurate program for transcript assembly of RNA-seq reads aligned to a reference genome. CLASS2 uses the splice graph model to represent a gene and its splice variants, and a dynamic programming optimization algorithm to score and select a subset of transcripts most likely present in the sample. | ||
+ | |- | ||
+ | |[https://github.com/gamcil/clinker clinker] | ||
+ | |0.0.27-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |clinker is a pipeline for easily generating publication-quality gene cluster comparison figures. | ||
|- | |- | ||
|[https://github.com/YeoLab/clipper clipper] | |[https://github.com/YeoLab/clipper clipper] | ||
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|- | |- | ||
|[http://www.clustal.org/omega/ Clustal-Omega] | |[http://www.clustal.org/omega/ Clustal-Omega] | ||
− | |1.2.4-IGB-gcc-4.9.4 | + | |1.2.4-IGB-gcc-4.9.4<br>1.2.4-IGB-gcc-8.2.0 |
− | |Clustal Omega is a multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. Evolutionary relationships can be seen via viewing Cladograms or Phylograms | + | |Clustal Omega is a multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. Evolutionary relationships can be seen via viewing Cladograms or Phylograms |
|- | |- | ||
|[http://www.ebi.ac.uk/Tools/msa/clustalw2/ ClustalW2] | |[http://www.ebi.ac.uk/Tools/msa/clustalw2/ ClustalW2] | ||
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|- | |- | ||
|[https://github.com/etal/cnvkit CNVkit] | |[https://github.com/etal/cnvkit CNVkit] | ||
− | |0.9.8-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.9.10-IGB-gcc-8.2.0-Python-3.10.1<br>0.9.8-IGB-gcc-8.2.0-Python-3.7.2 |
|A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from high-throughput sequencing. | |A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from high-throughput sequencing. | ||
|- | |- | ||
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|0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | |0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|The tools for your .cools | |The tools for your .cools | ||
+ | |- | ||
+ | |[https://github.com/Zhao-Group/COPIES COPIES] | ||
+ | |20231202-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites (CRISPR-COPIES) is a user-friendly web application and a command line tool for rapid discovery of neutral integration sites. | ||
|- | |- | ||
|[http://www.gnu.org/software/coreutils/coreutils.html coreutils] | |[http://www.gnu.org/software/coreutils/coreutils.html coreutils] | ||
− | |8.28-IGB-gcc-4.9.4<br>8.28-IGB-gcc-8.2.0 | + | |8.28-IGB-gcc-4.9.4<br>8.28-IGB-gcc-8.2.0<br>9.1-IGB-gcc-8.2.0 |
|The GNU Core Utilities are the basic file, shell and text manipulation utilities of the GNU operating system.These are the core utilities which are expected to exist on every operating system. | |The GNU Core Utilities are the basic file, shell and text manipulation utilities of the GNU operating system.These are the core utilities which are expected to exist on every operating system. | ||
|- | |- | ||
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|1.0.5.21-IGB-gcc-4.9.4 | |1.0.5.21-IGB-gcc-4.9.4 | ||
|reference free variant assembly | |reference free variant assembly | ||
+ | |- | ||
+ | |CppUnit | ||
+ | | | ||
+ | |CppUnit is the C++ port of the famous JUnit framework for unit testing. | ||
|- | |- | ||
|[https://github.com/cboursnell/crb-blast crb-blast] | |[https://github.com/cboursnell/crb-blast crb-blast] | ||
|0.6.9-IGB-gcc-4.9.4 | |0.6.9-IGB-gcc-4.9.4 | ||
|Conditional Reciprocal Best BLAST - high confidence ortholog assignment. CRB-BLAST is a novel method for finding orthologs between one set of sequences and another. This is particularly useful in genome and transcriptome annotation. | |Conditional Reciprocal Best BLAST - high confidence ortholog assignment. CRB-BLAST is a novel method for finding orthologs between one set of sequences and another. This is particularly useful in genome and transcriptome annotation. | ||
+ | |- | ||
+ | |[https://www.animalgenome.org/tools/share/crimap/ crimap] | ||
+ | |2.507-IGB-gcc-8.2.0 | ||
+ | |CRI-MAP (version 2.4, by Phil Green et al, 1990) has been used extensively in the past 20 years for genetic linkage analysis of diploid species, and has played a fundamental role in producing genetic linkage maps for humans, rats, mouse, fruit flies, cattle, sheep, pigs, chicken, fish, among many other species. | ||
|- | |- | ||
|[https://github.com/broadinstitute/cromwell cromwell] | |[https://github.com/broadinstitute/cromwell cromwell] | ||
|39-Java-1.8.0_152 | |39-Java-1.8.0_152 | ||
|Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments | |Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments | ||
+ | |- | ||
+ | |[https://crossmap.sourceforge.net/ Crossmap] | ||
+ | |0.6.5-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |CrossMap is a program for genome coordinates conversion between different assemblies (such as hg18 (NCBI36) <=> hg19 (GRCh37)). It supports commonly used file formats including BAM, CRAM, SAM, Wiggle, BigWig, BED, GFF, GTF, MAF VCF, and gVCF. | ||
|- | |- | ||
|[https://developer.nvidia.com/cuda-toolkit CUDA] | |[https://developer.nvidia.com/cuda-toolkit CUDA] | ||
− | |10.0.130<br>10.1.105<br>11.1.0<br>8.0.61<br>8.0.61-IGB-gcc-4.9.4<br>9.0.176<br>9.1.85 | + | |10.0.130<br>10.1.105<br>11.0.3<br>11.1.0<br>11.2.2<br>11.3.0<br>11.8.0<br>8.0.61<br>8.0.61-IGB-gcc-4.9.4<br>9.0.176<br>9.1.85 |
|CUDA (formerly Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce. CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. | |CUDA (formerly Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce. CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. | ||
|- | |- | ||
|[https://developer.nvidia.com/cudnn cuDNN] | |[https://developer.nvidia.com/cudnn cuDNN] | ||
− | |5.1-CUDA-8.0.61<br>5.1-IGB-gcc-4.9.4-CUDA-8.0.61<br>6.0-IGB-gcc-4.9.4-CUDA-8.0.61<br>7.0.5-CUDA-9.0.176<br>7.1.4-CUDA-9.0.176<br>7.6.1.34-CUDA-10.0.130<br>8.0.4.30-CUDA-10.1.105<br>8.0.4.30-CUDA-11.1.0 | + | |5.1-CUDA-8.0.61<br>5.1-IGB-gcc-4.9.4-CUDA-8.0.61<br>6.0-IGB-gcc-4.9.4-CUDA-8.0.61<br>7.0.5-CUDA-9.0.176<br>7.1.4-CUDA-9.0.176<br>7.6.1.34-CUDA-10.0.130<br>8.0.4.30-CUDA-10.1.105<br>8.0.4.30-CUDA-11.1.0<br>8.1.1.33-CUDA-11.2.2<br>8.2.1.32-CUDA-11.3.0<br>8.9.2.23-CUDA-11.8.0 |
|The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. | |The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. | ||
|- | |- | ||
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|- | |- | ||
|[http://opensource.scilifelab.se/projects/cutadapt/ cutadapt] | |[http://opensource.scilifelab.se/projects/cutadapt/ cutadapt] | ||
− | |1.14-IGB-gcc-4.9.4-Python-2.7.13<br>1.17-IGB-gcc-4.9.4-Python-3.6.1<br>2.10-IGB-gcc-8.2.0-Python-3.7.2 | + | |1.14-IGB-gcc-4.9.4-Python-2.7.13<br>1.17-IGB-gcc-4.9.4-Python-3.6.1<br>2.10-IGB-gcc-8.2.0-Python-3.7.2<br>3.7-IGB-gcc-8.2.0-Python-3.7.2 |
|Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads. | |Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads. | ||
|- | |- | ||
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|- | |- | ||
|[https://cytoscape.org/ cytoscape] | |[https://cytoscape.org/ cytoscape] | ||
− | |3.7.0-Java-1.8.0_152<br>3.8.2-Java-11.0.5 | + | |3.7.0-Java-1.8.0_152<br>3.8.2-Java-11.0.5<br>3.9.1-Java-11.0.5 |
|Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. | |Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. | ||
|- | |- | ||
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|- | |- | ||
|[http://www.mousemotorlab.org/deeplabcut deeplabcut] | |[http://www.mousemotorlab.org/deeplabcut deeplabcut] | ||
− | |2.1.8.2-IGB-gcc-4.9.4-Python-3.6.1 | + | |2.1.8.2-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.1.1-IGB-gcc-8.2.0-Python-3.7.2 |
|Markerless pose estimation of user-defined features with deep learning for all animals | |Markerless pose estimation of user-defined features with deep learning for all animals | ||
|- | |- | ||
|[https://github.com/fidelram/deepTools deepTools] | |[https://github.com/fidelram/deepTools deepTools] | ||
− | |2.5.3-IGB-gcc-4.9.4-Python-2.7.13<br>3.0.1-IGB-gcc-4.9.4-Python-2.7.13<br>3.2.1-IGB-gcc-4.9.4-Python-3.6.1 | + | |2.5.3-IGB-gcc-4.9.4-Python-2.7.13<br>3.0.1-IGB-gcc-4.9.4-Python-2.7.13<br>3.2.1-IGB-gcc-4.9.4-Python-3.6.1<br>3.5.2-IGB-gcc-4.9.4-Python-3.6.1<br>3.5.2-IGB-gcc-8.2.0-Python-3.7.2 |
|deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. deepTools contains useful modules to process the mapped reads data for multiple quality checks, creating normalized coverage files in standard bedGraph and bigWig file formats, that allow comparison between different files (for example, treatment and control). Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome. | |deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. deepTools contains useful modules to process the mapped reads data for multiple quality checks, creating normalized coverage files in standard bedGraph and bigWig file formats, that allow comparison between different files (for example, treatment and control). Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome. | ||
|- | |- | ||
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|4.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>4.4.0-IGB-gcc-8.2.0-Python-3.7.2 | |4.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>4.4.0-IGB-gcc-8.2.0-Python-3.7.2 | ||
|DendroPy is a Python library for phylogenetic computing. | |DendroPy is a Python library for phylogenetic computing. | ||
+ | |- | ||
+ | |[https://gitee.com/bxxu/denopa deNOPA] | ||
+ | |1.0.2-IGB-gcc-4.9.4-Python-2.7.13 | ||
+ | |As the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. | ||
|- | |- | ||
|[https://github.com/facebookresearch/detectron2/ detectron2] | |[https://github.com/facebookresearch/detectron2/ detectron2] | ||
− | |0.2.1-IGB-gcc-4.9.4-Python-3.6.1<br>0.2.1-IGB-gcc-8.2.0-Python-3.7.2<br>0.5-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.2.1-IGB-gcc-4.9.4-Python-3.6.1<br>0.2.1-IGB-gcc-8.2.0-Python-3.7.2<br>0.5-IGB-gcc-8.2.0-Python-3.7.2<br>0.6-IGB-gcc-8.2.0-Python-3.7.2 |
|Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms | |Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms | ||
|- | |- | ||
|[https://github.com/bbuchfink/diamond DIAMOND] | |[https://github.com/bbuchfink/diamond DIAMOND] | ||
− | |0.8.38-IGB-gcc-4.9.4<br>0.9.10-IGB-gcc-4.9.4<br>0.9.16-IGB-gcc-4.9.4<br>0.9.22-IGB-gcc-4.9.4<br>0.9.24-IGB-gcc-4.9.4<br>0.9.24-IGB-gcc-8.2.0<br>0.9.9-IGB-gcc-4.9.4<br>2.0.6-IGB-gcc-8.2.0<br>2.0.9-IGB-gcc-8.2.0 | + | |0.8.38-IGB-gcc-4.9.4<br>0.9.10-IGB-gcc-4.9.4<br>0.9.16-IGB-gcc-4.9.4<br>0.9.22-IGB-gcc-4.9.4<br>0.9.24-IGB-gcc-4.9.4<br>0.9.24-IGB-gcc-8.2.0<br>0.9.36-IGB-gcc-8.2.0<br>0.9.9-IGB-gcc-4.9.4<br>2.0.15-IGB-gcc-8.2.0<br>2.0.6-IGB-gcc-8.2.0<br>2.0.9-IGB-gcc-8.2.0 |
|Accelerated BLAST compatible local sequence aligner | |Accelerated BLAST compatible local sequence aligner | ||
|- | |- | ||
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|2.1.0 | |2.1.0 | ||
|a user-friendly approach to Approximate Bayesian Computation for inference on population history using molecular markers | |a user-friendly approach to Approximate Bayesian Computation for inference on population history using molecular markers | ||
+ | |- | ||
+ | |[https://github.com/diyabc/diyabc diyabc] | ||
+ | |1.1.28 | ||
+ | |DIYABC RF V1.0 | ||
|- | |- | ||
|[https://jbloomlab.github.io/dms_tools2/ dms-tools2] | |[https://jbloomlab.github.io/dms_tools2/ dms-tools2] | ||
|2.6.10-IGB-gcc-8.2.0-Python-3.7.2 | |2.6.10-IGB-gcc-8.2.0-Python-3.7.2 | ||
|dms_tools2 is a software package for analyzing deep mutational scanning data. It is tailored to analyze libraries created using comprehensive codon mutagenesis of protein-coding genes, and perform analyses that are common to the Bloom lab, | |dms_tools2 is a software package for analyzing deep mutational scanning data. It is tailored to analyze libraries created using comprehensive codon mutagenesis of protein-coding genes, and perform analyses that are common to the Bloom lab, | ||
+ | |- | ||
+ | |[https://github.com/nanoporetech/dorado dorado] | ||
+ | |0.6.0 | ||
+ | |Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads. | ||
|- | |- | ||
|[http://www.doxygen.org Doxygen] | |[http://www.doxygen.org Doxygen] | ||
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|dRep is a python program which performs rapid pair-wise comparison of genome sets. One of it’s major purposes is for genome de-replication, but it can do a lot more. | |dRep is a python program which performs rapid pair-wise comparison of genome sets. One of it’s major purposes is for genome de-replication, but it can do a lot more. | ||
|- | |- | ||
− | |[https://github.com/SpectraLogic/ds3_java_cli/ | + | |[https://github.com/SpectraLogic/ds3_java_cli/ ds3_java_cli] |
− | |5.1.2<br>5.1. | + | |5.1.2<br>5.1.4 |
|Command line utilities for Bioarchive | |Command line utilities for Bioarchive | ||
|- | |- | ||
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|Command-line tools for processing biological sequencing data. Barcode demultiplexing, adapter trimming, etc. Primarily written to support an Illumina based pipeline - but should work with any FASTQs. | |Command-line tools for processing biological sequencing data. Barcode demultiplexing, adapter trimming, etc. Primarily written to support an Illumina based pipeline - but should work with any FASTQs. | ||
|- | |- | ||
− | |[ | + | |[https://easybuilders.github.io/easybuild EasyBuild] |
− | |4. | + | |4.6.2 |
− | |EasyBuild is a software build and installation | + | |EasyBuild is a software build and installation framework written in Python that allows you to install software in a structured, repeatable and robust way. |
|- | |- | ||
|[https://github.com/Rostlab/EAT EAT] | |[https://github.com/Rostlab/EAT EAT] | ||
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|Entrez Direct (EDirect) provides access to the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a UNIX terminal window. | |Entrez Direct (EDirect) provides access to the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a UNIX terminal window. | ||
|- | |- | ||
− | | | + | |[https://sccn.ucsd.edu/eeglab/index.php eeglab] |
− | | | + | |2021.1-IGB-gcc-8.2.0 |
− | | | + | |EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. |
|- | |- | ||
− | | | + | |[https://github.com/eggnogdb/eggnog-mapper eggnog-mapper] |
− | | | + | |2.1.12-IGB-gcc-8.2.0-Python-3.7.2 |
− | | | + | |EggNOG-mapper is a tool for fast functional annotation of novel sequences. |
|- | |- | ||
− | | | + | |Eigen |
− | |||
| | | | ||
+ | |Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. | ||
|- | |- | ||
− | + | |[https://github.com/DReichLab/EIG eigensoft] | |
− | + | |7.2.1-IGB-gcc-4.9.4 | |
− | + | |The EIGENSOFT package combines functionality from our population genetics methods (Patterson et al. 2006) and our EIGENSTRAT stratification correction method (Price et al. 2006). | |
− | |||
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− | |||
− | |[https://github.com/DReichLab/EIG eigensoft] | ||
− | |7.2.1-IGB-gcc-4.9.4 | ||
− | |The EIGENSOFT package combines functionality from our population genetics methods (Patterson et al. 2006) and our EIGENSTRAT stratification correction method (Price et al. 2006). | ||
|- | |- | ||
|[http://emboss.sourceforge.net/ EMBOSS] | |[http://emboss.sourceforge.net/ EMBOSS] | ||
Line 776: | Line 832: | ||
|0.1-IGB-gcc-8.2.0-Python-3.7.2 | |0.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|Evolocity is a Python package that implements evolutionary velocity, which constructs landscapes of protein evolution by using the local evolutionary predictions enabled by language models to predict the directionality of evolution and is described in the paper "Evolutionary velocity with protein language models" by Brian Hie, Kevin Yang, and Peter Kim. | |Evolocity is a Python package that implements evolutionary velocity, which constructs landscapes of protein evolution by using the local evolutionary predictions enabled by language models to predict the directionality of evolution and is described in the paper "Evolutionary velocity with protein language models" by Brian Hie, Kevin Yang, and Peter Kim. | ||
+ | |- | ||
+ | |[https://exiftool.org/ exiftool] | ||
+ | |12.54-IGB-gcc-8.2.0-Perl-5.28.1 | ||
+ | |Read, Write and Edit Meta Information! | ||
|- | |- | ||
|[https://www.ebi.ac.uk/about/vertebrate-genomics/software/exonerate exonerate] | |[https://www.ebi.ac.uk/about/vertebrate-genomics/software/exonerate exonerate] | ||
|2.2.0-IGB-gcc-4.9.4<br>2.2.0-IGB-gcc-8.2.0 | |2.2.0-IGB-gcc-4.9.4<br>2.2.0-IGB-gcc-8.2.0 | ||
|Exonerate is a generic tool for pairwise sequence comparison. It allows you to align sequences using a many alignment models, either exhaustive dynamic programming or a variety of heuristics. | |Exonerate is a generic tool for pairwise sequence comparison. It allows you to align sequences using a many alignment models, either exhaustive dynamic programming or a variety of heuristics. | ||
+ | |- | ||
+ | |[https://gitlab.com/ExonOntology/ExonOntology ExonOntology] | ||
+ | |20171018-IGB-gcc-8.2.0-Perl-5.28.1 | ||
+ | |This project consists of a set of scripts that are necessary to perform offline Exon Ontology analyses. Briefly, it can accept a list of genomic sequences as input that represent exons (or parts of exons). The algorithm then retrieves the protein features that are encoded by these DNA sequences. | ||
|- | |- | ||
|expat | |expat | ||
Line 786: | Line 850: | ||
|- | |- | ||
|[https://github.com/facebookresearch/esm#available fair-esm] | |[https://github.com/facebookresearch/esm#available fair-esm] | ||
− | |0.4.0-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.4.0-IGB-gcc-8.2.0-Python-3.7.2<br>2.0.0-IGB-gcc-8.2.0-Python-3.7.2 |
|This repository contains code and pre-trained weights for Transformer protein language models from Facebook AI Research, including our state-of-the-art ESM-1b and MSA Transformer. | |This repository contains code and pre-trained weights for Transformer protein language models from Facebook AI Research, including our state-of-the-art ESM-1b and MSA Transformer. | ||
|- | |- | ||
Line 804: | Line 868: | ||
|0.6.2-IGB-gcc-4.9.4<br>0.6.3-IGB-gcc-4.9.4<br>0.6.5-IGB-gcc-4.9.4<br>0.6.5-IGB-gcc-8.2.0 | |0.6.2-IGB-gcc-4.9.4<br>0.6.3-IGB-gcc-4.9.4<br>0.6.5-IGB-gcc-4.9.4<br>0.6.5-IGB-gcc-8.2.0 | ||
|A lightweight C++ library for accessing Oxford Nanopore Technologies sequencing data. | |A lightweight C++ library for accessing Oxford Nanopore Technologies sequencing data. | ||
+ | |- | ||
+ | |[https://fasta.bioch.virginia.edu/wrpearson/fasta/fasta2/ fasta2] | ||
+ | |21.1.1-IGB-gcc-8.2.0 | ||
+ | | | ||
|- | |- | ||
|[https://github.com/ParBliSS/FastANI FastANI] | |[https://github.com/ParBliSS/FastANI FastANI] | ||
Line 810: | Line 878: | ||
|- | |- | ||
|[http://www.atgc-montpellier.fr/fastme/binaries.php FastME] | |[http://www.atgc-montpellier.fr/fastme/binaries.php FastME] | ||
− | |2.1.6.1-IGB-gcc-4.9.4 | + | |2.1.6.1-IGB-gcc-4.9.4<br>2.1.6.3-IGB-gcc-8.2.0 |
|FastME provides distance algorithms to infer phylogenies. FastME is based on balanced minimum evolution, which is the very principle of NJ. FastME improves over NJ by performing topological moves using fast, sophisticated algorithms. | |FastME provides distance algorithms to infer phylogenies. FastME is based on balanced minimum evolution, which is the very principle of NJ. FastME improves over NJ by performing topological moves using fast, sophisticated algorithms. | ||
+ | |- | ||
+ | |[https://miso.readthedocs.io/en/fastmiso/index.html fastmiso] | ||
+ | |0.5.4-IGB-gcc-4.9.4-Python-2.7.13 | ||
+ | |MISO (Mixture-of-Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. | ||
|- | |- | ||
|[https://github.com/OpenGene/fastp fastp] | |[https://github.com/OpenGene/fastp fastp] | ||
− | |0.19.5-IGB-gcc-4.9.4<br>0.19.6-IGB-gcc-4.9.4-7117eba<br>0.20.0-IGB-gcc-4.9.4 | + | |0.19.5-IGB-gcc-4.9.4<br>0.19.6-IGB-gcc-4.9.4-7117eba<br>0.20.0-IGB-gcc-4.9.4<br>0.23.4 |
|A tool designed to provide fast all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported to afford high performance. | |A tool designed to provide fast all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported to afford high performance. | ||
|- | |- | ||
Line 826: | Line 898: | ||
|- | |- | ||
|[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ FastQC] | |[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ FastQC] | ||
− | |0.11.5 | + | |0.11.5-Java-1.8.0_201<br>0.11.8-Java-1.8.0_152<br>0.11.9-Java-1.8.0_201 |
|FastQC is a quality control application for high throughput sequence data. It reads in sequence data in a variety of formats and can either provide an interactive application to review the results of several different QC checks, or create an HTML based report which can be integrated into a pipeline. | |FastQC is a quality control application for high throughput sequence data. It reads in sequence data in a variety of formats and can either provide an interactive application to review the results of several different QC checks, or create an HTML based report which can be integrated into a pipeline. | ||
|- | |- | ||
Line 902: | Line 974: | ||
|- | |- | ||
|[https://github.com/fenderglass/Flye Flye] | |[https://github.com/fenderglass/Flye Flye] | ||
− | |2.4.2-IGB-gcc-4.9.4-Python-2.7.13<br>2.7-IGB-gcc-4.9.4-Python-3.6.1<br>2.7.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.8.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.8.2-IGB-gcc-8.2.0-Python-3.7.2<br>2.9-IGB-gcc-8.2.0-Python-3.7.2 | + | |2.4.2-IGB-gcc-4.9.4-Python-2.7.13<br>2.7-IGB-gcc-4.9.4-Python-3.6.1<br>2.7.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.8.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.8.2-IGB-gcc-8.2.0-Python-3.7.2<br>2.9-IGB-gcc-8.2.0-Python-3.7.2<br>2.9.2-IGB-gcc-8.2.0-Python-3.7.2 |
|Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. | |Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. | ||
+ | |- | ||
+ | |[https://github.com/steineggerlab/foldseek foldseek] | ||
+ | |8-ef4e960 | ||
+ | |Foldseek enables fast and sensitive comparisons of large structure sets. | ||
+ | |- | ||
+ | |[https://foldxsuite.crg.eu/ foldx] | ||
+ | |5.0 | ||
+ | |The FoldX Suite builds on the strong fundament of advanced protein design features, already implemented in the successful FoldX3, and exploits the power of fragment libraries, by integrating in silico digested backbone protein fragments of different lengths. S | ||
|- | |- | ||
|fontconfig | |fontconfig | ||
Line 928: | Line 1,008: | ||
| | | | ||
|FreeType 2 is a software font engine that is designed to be small, efficient, highly customizable, and portable while capable of producing high-quality output (glyph images). It can be used in graphics libraries, display servers, font conversion tools, text image generation tools, and many other products as well. | |FreeType 2 is a software font engine that is designed to be small, efficient, highly customizable, and portable while capable of producing high-quality output (glyph images). It can be used in graphics libraries, display servers, font conversion tools, text image generation tools, and many other products as well. | ||
+ | |- | ||
+ | |FriBidi | ||
+ | | | ||
+ | |The Free Implementation of the Unicode Bidirectional Algorithm. | ||
|- | |- | ||
|[http://fureylab.web.unc.edu/software/fseq/ fseq] | |[http://fureylab.web.unc.edu/software/fseq/ fseq] | ||
Line 940: | Line 1,024: | ||
|0.6.1-IGB-gcc-8.2.0 | |0.6.1-IGB-gcc-8.2.0 | ||
|gappa is a collection of commands for working with phylogenetic data. Its main focus are evolutionary placements of short environmental sequences on a reference phylogenetic tree. Such data is typically produced by tools like EPA-ng, RAxML-EPA or pplacer and usually stored in jplace files. | |gappa is a collection of commands for working with phylogenetic data. Its main focus are evolutionary placements of short environmental sequences on a reference phylogenetic tree. Such data is typically produced by tools like EPA-ng, RAxML-EPA or pplacer and usually stored in jplace files. | ||
+ | |- | ||
+ | |[https://github.com/simoncchu/GAPPadder GAPPadder] | ||
+ | |20170601-IGB-gcc-4.9.4 | ||
+ | |GAPPadder is designed for closing gaps on the draft genomes with paired-end reads or mate-paired reads. | ||
|- | |- | ||
|[https://github.com/jotech/gapseq gapseq] | |[https://github.com/jotech/gapseq gapseq] | ||
Line 946: | Line 1,034: | ||
|- | |- | ||
|[http://www.broadinstitute.org/gatk/ GATK] | |[http://www.broadinstitute.org/gatk/ GATK] | ||
− | |3.7-Java-1.8.0_121<br>3.8-0-Java-1.8.0_121<br>3.8-0-Java-1.8.0_152<br>3.8-1-0-Java-1.8.0_152<br>4.0.4.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1<br>4.0.9.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1<br>4.1.4.0-Java-1.8.0_152<br>4.2.4.1-Java-1.8.0_201 | + | |3.7-Java-1.8.0_121<br>3.8-0-Java-1.8.0_121<br>3.8-0-Java-1.8.0_152<br>3.8-1-0-Java-1.8.0_152<br>4.0.4.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1<br>4.0.9.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1<br>4.1.4.0-Java-1.8.0_152<br>4.2.4.1-Java-1.8.0_201<br>4.2.6.1-Java-1.8.0_201<br>4.4.0.0-Java-17.0.6 |
|Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. | |Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. | ||
+ | |- | ||
+ | |[https://gaussian.com/gaussian16/ Gaussian] | ||
+ | |16.C.01 | ||
+ | |Gaussian 16 is the latest in the Gaussian series of programs. It provides state-of-the-art capabilities for electronic structure modeling. Gaussian 16 is licensed for a wide variety of computer systems. All versions of Gaussian 16 contain every scientific/modeling feature, and none imposes any artificial limitations on calculations other than your computing resources and patience. | ||
|- | |- | ||
|[https://github.com/halelab/GBS-SNP-CROP GBS-SNP-CROP] | |[https://github.com/halelab/GBS-SNP-CROP GBS-SNP-CROP] | ||
|4.0-IGB-gcc-4.9.4 | |4.0-IGB-gcc-4.9.4 | ||
|The GBS SNP Calling Reference Optional Pipeline (GBS-SNP-CROP) is executed via a sequence of seven Perl scripts that integrate custom parsing and filtering procedures with well-known, vetted bioinformatic tools, giving the user full access to all intermediate files. | |The GBS SNP Calling Reference Optional Pipeline (GBS-SNP-CROP) is executed via a sequence of seven Perl scripts that integrate custom parsing and filtering procedures with well-known, vetted bioinformatic tools, giving the user full access to all intermediate files. | ||
+ | |- | ||
+ | |[https://github.com/GenomicsCoreLeuven/GBSX GBSX] | ||
+ | |1.3-IGB-gcc-8.2.0-Java-1.8.0_201 | ||
+ | |Genotyping by Sequencing is an emerging technology for cost effective variant discovery and genotyping. However, current analysis tools do not fulfill all experimental design and analysis needs. | ||
|- | |- | ||
|GCC | |GCC | ||
Line 964: | Line 1,060: | ||
|1.0.2-IGB-gcc-8.2.0 | |1.0.2-IGB-gcc-8.2.0 | ||
|GCE (genomic charactor estimator) is a bayes model based method to estimate the genome size, genomic repeat content and the heterozygsis rate of the sequencingsample | |GCE (genomic charactor estimator) is a bayes model based method to estimate the genome size, genomic repeat content and the heterozygsis rate of the sequencingsample | ||
+ | |- | ||
+ | |[https://yanglab.westlake.edu.cn/software/gcta gcta] | ||
+ | |1.94.0Beta | ||
+ | |GCTA (Genome-wide Complex Trait Analysis) is a software package initially developed to estimate the proportion of phenotypic variance explained by all genome-wide SNPs for a complex trait but has been greatly extended for many other analyses of data from genome-wide association studies (GWASs). | ||
|- | |- | ||
|[http://search.cpan.org/~lds/GD/ GD] | |[http://search.cpan.org/~lds/GD/ GD] | ||
Line 1,054: | Line 1,154: | ||
|- | |- | ||
|gffutils | |gffutils | ||
− | |0.10.1-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.10.1-IGB-gcc-8.2.0-Python-3.7.2<br>0.11.1-IGB-gcc-8.2.0-Python-3.7.2 |
| | | | ||
|- | |- | ||
|[https://github.com/gflags/gflags gflags] | |[https://github.com/gflags/gflags gflags] | ||
− | |2.2.2-IGB-gcc-4.9.4 | + | |2.2.2-IGB-gcc-4.9.4<br>2.2.2-IGB-gcc-8.2.0 |
|The gflags package contains a C++ library that implements commandline flagsprocessing. It includes built-in support for standard types such as stringand the ability to define flags in the source file in which they are used. | |The gflags package contains a C++ library that implements commandline flagsprocessing. It includes built-in support for standard types such as stringand the ability to define flags in the source file in which they are used. | ||
|- | |- | ||
Line 1,092: | Line 1,192: | ||
|3.0.4-IGB-gcc-4.9.4 | |3.0.4-IGB-gcc-4.9.4 | ||
|GlimmerHMM is a new gene finder based on a Generalized Hidden Markov Model. Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models. | |GlimmerHMM is a new gene finder based on a Generalized Hidden Markov Model. Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models. | ||
+ | |- | ||
+ | |[https://docs.globus.org/cli/ globus-cli] | ||
+ | |3.10.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.18.0-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |The CLI provides an interface to Globus services from the shell, and is suited to both interactive and simple scripting use cases. | ||
|- | |- | ||
|[https://github.com/google/glog glog] | |[https://github.com/google/glog glog] | ||
− | |0.4.0-IGB-gcc-4.9.4 | + | |0.4.0-IGB-gcc-4.9.4<br>0.5.0-IGB-gcc-8.2.0 |
|A C++ implementation of the Google logging module. | |A C++ implementation of the Google logging module. | ||
|- | |- | ||
Line 1,119: | Line 1,223: | ||
|[http://gnuplot.sourceforge.net/ gnuplot] | |[http://gnuplot.sourceforge.net/ gnuplot] | ||
|4.6.7-IGB-gcc-4.9.4<br>4.6.7-IGB-gcc-8.2.0<br>5.0.6-IGB-gcc-4.9.4 | |4.6.7-IGB-gcc-4.9.4<br>4.6.7-IGB-gcc-8.2.0<br>5.0.6-IGB-gcc-4.9.4 | ||
− | |Portable interactive, function plotting utility | + | |Portable interactive, function plotting utility |
|- | |- | ||
|GObject-Introspection | |GObject-Introspection | ||
Line 1,138: | Line 1,242: | ||
|- | |- | ||
|[https://www.gnu.org/software/gperf/ gperf] | |[https://www.gnu.org/software/gperf/ gperf] | ||
− | |3.1-IGB-gcc-4.9.4 | + | |3.1-IGB-gcc-4.9.4<br>3.1-IGB-gcc-8.2.0 |
|GNU gperf is a perfect hash function generator. For a given list of strings, it produces a hash function and hash table, in form of C or C++ code, for looking up a value depending on the input string. The hash function is perfect, which means that the hash table has no collisions, and the hash table lookup needs a single string comparison only. | |GNU gperf is a perfect hash function generator. For a given list of strings, it produces a hash function and hash table, in form of C or C++ code, for looking up a value depending on the input string. The hash function is perfect, which means that the hash table has no collisions, and the hash table lookup needs a single string comparison only. | ||
|- | |- | ||
Line 1,178: | Line 1,282: | ||
|- | |- | ||
|[https://ecogenomics.github.io/GTDBTk/ GTDBTk] | |[https://ecogenomics.github.io/GTDBTk/ GTDBTk] | ||
− | |1.5.0 | + | |1.5.0<br>2.1.1<br>2.3.0 |
|GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB | |GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB | ||
|- | |- | ||
Line 1,196: | Line 1,300: | ||
|2.02-IGB-gcc-4.9.4-Perl-5.24.1 | |2.02-IGB-gcc-4.9.4-Perl-5.24.1 | ||
|GUIDANCE is meant to be used for weighting, filtering or masking unreliably aligned positions in sequence alignments before subsequent analysis. For example, align codon sequences (nucleotide sequences that code for proteins) using PAGAN, remove columns with low GUIDANCE scores, and use the remaining alignment to infer positive selection using the branch-site dN/dS test. Other analyses where GUIDANCE filtering could be useful include phylogeny reconstruction, reconstruction of the history of specific insertion and deletion events, inference of recombination events, etc. - Homepage: http://guidance.tau.ac.il/ver2/ | |GUIDANCE is meant to be used for weighting, filtering or masking unreliably aligned positions in sequence alignments before subsequent analysis. For example, align codon sequences (nucleotide sequences that code for proteins) using PAGAN, remove columns with low GUIDANCE scores, and use the remaining alignment to infer positive selection using the branch-site dN/dS test. Other analyses where GUIDANCE filtering could be useful include phylogeny reconstruction, reconstruction of the history of specific insertion and deletion events, inference of recombination events, etc. - Homepage: http://guidance.tau.ac.il/ver2/ | ||
+ | |- | ||
+ | |[https://github.com/tsailabSJ/guideseq guideseq] | ||
+ | |20190913-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |The guideseq package implements our data preprocessing and analysis pipeline for GUIDE-Seq data. It takes raw sequencing reads (FASTQ) and a parameter manifest file (.yaml) as input and produces a table of annotated off-target sites as output. | ||
|- | |- | ||
|[https://community.nanoporetech.com/posts/pre-release-of-stand-alone guppy] | |[https://community.nanoporetech.com/posts/pre-release-of-stand-alone guppy] | ||
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|7.5.2 | |7.5.2 | ||
|The Gurobi Optimizer was designed from the ground up to be the fastest, most powerful solver available for your LP, QP, QCP, and MIP (MILP, MIQP, and MIQCP) problems. | |The Gurobi Optimizer was designed from the ground up to be the fastest, most powerful solver available for your LP, QP, QCP, and MIP (MILP, MIQP, and MIQCP) problems. | ||
+ | |- | ||
+ | |[https://github.com/Gaius-Augustus/GUSHR GUSHR] | ||
+ | |20200928-Java-1.8.0_201 | ||
+ | |Assembly-free construction of UTRs from short read RNA-Seq data on the basis of coding sequence annotation. | ||
|- | |- | ||
|[https://www.aaronrenn.com/arenn/hacking/gzrt/gzrt.html gzrt] | |[https://www.aaronrenn.com/arenn/hacking/gzrt/gzrt.html gzrt] | ||
Line 1,250: | Line 1,362: | ||
|- | |- | ||
|[https://hicexplorer.readthedocs.io/en/latest/index.html HiCExplorer] | |[https://hicexplorer.readthedocs.io/en/latest/index.html HiCExplorer] | ||
− | |2.2.1.1-IGB-gcc-8.2.0-Python-3.7.2 | + | |2.2.1.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.7.2-IGB-gcc-8.2.0-Python-3.7.2 |
|HiCExplorer addresses the common tasks of Hi-C data analysis from processing to visualization. | |HiCExplorer addresses the common tasks of Hi-C data analysis from processing to visualization. | ||
|- | |- | ||
|[https://github.com/chhylp123/hifiasm hifiasm] | |[https://github.com/chhylp123/hifiasm hifiasm] | ||
− | |0.13-IGB-gcc-8.2.0<br>0.14.2-IGB-gcc-8.2.0<br>0.15-IGB-gcc-8.2.0<br>0.16.1-IGB-gcc-8.2.0<br>0.5-IGB-gcc-8.2.0 | + | |0.13-IGB-gcc-8.2.0<br>0.14.2-IGB-gcc-8.2.0<br>0.15-IGB-gcc-8.2.0<br>0.16.1-IGB-gcc-8.2.0<br>0.18.1-IGB-gcc-8.2.0<br>0.19.5-IGB-gcc-8.2.0<br>0.19.6-IGB-gcc-8.2.0<br>0.5-IGB-gcc-8.2.0 |
|Hifiasm is a fast haplotype-resolved de novo assembler for PacBio Hifi reads. Unlike most existing assemblers, hifiasm starts from uncollapsed genome. Thus, it is able to keep the haplotype information as much as possible. | |Hifiasm is a fast haplotype-resolved de novo assembler for PacBio Hifi reads. Unlike most existing assemblers, hifiasm starts from uncollapsed genome. Thus, it is able to keep the haplotype information as much as possible. | ||
+ | |- | ||
+ | |[https://github.com/xfengnefx/hifiasm-meta hifiasm-meta] | ||
+ | |0.3-IGB-gcc-8.2.0 | ||
+ | |A hifiasm fork for metagenome assembly using Hifi reads | ||
|- | |- | ||
|[https://github.com/higlass/higlass-python higlass-python] | |[https://github.com/higlass/higlass-python higlass-python] | ||
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|2.0.5-IGB-gcc-4.9.4<br>2.1.0-IGB-gcc-4.9.4<br>2.2.0-IGB-gcc-4.9.4<br>2.2.1-IGB-gcc-8.2.0-Python-3.7.2 | |2.0.5-IGB-gcc-4.9.4<br>2.1.0-IGB-gcc-4.9.4<br>2.2.0-IGB-gcc-4.9.4<br>2.2.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) against the general human population (as well as against a single reference genome). | |HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) against the general human population (as well as against a single reference genome). | ||
+ | |- | ||
+ | |[http://daehwankimlab.github.io/hisat2/hisat-3n/ HISAT2-3N] | ||
+ | |20221013-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |HISAT-3N (hierarchical indexing for spliced alignment of transcripts - 3 nucleotides) is designed for nucleotide conversion sequencing technologies and implemented based on HISAT2. | ||
|- | |- | ||
|[http://hmmer.org/ HMMER] | |[http://hmmer.org/ HMMER] | ||
− | |2.3.2-IGB-gcc-4.9.4<br>3.1b2-IGB-gcc-4.9.4<br>3.2.1-IGB-gcc-4.9.4<br>3.3.1-IGB-gcc-8.2.0 | + | |2.3.2-IGB-gcc-4.9.4<br>2.3.2-IGB-gcc-8.2.0<br>3.1b2-IGB-gcc-4.9.4<br>3.2.1-IGB-gcc-4.9.4<br>3.3.1-IGB-gcc-8.2.0 |
|HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST. | |HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST. | ||
|- | |- | ||
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|- | |- | ||
|[http://www.htslib.org/ HTSlib] | |[http://www.htslib.org/ HTSlib] | ||
− | |1.10.2-IGB-gcc-8.2.0<br>1.11-IGB-gcc-8.2.0<br>1.12-IGB-gcc-8.2.0<br>1.4-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4<br>1.9-IGB-gcc-8.2.0 | + | |1.10.2-IGB-gcc-8.2.0<br>1.11-IGB-gcc-8.2.0<br>1.12-IGB-gcc-8.2.0<br>1.17-IGB-gcc-8.2.0<br>1.4-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4<br>1.9-IGB-gcc-8.2.0 |
|A C library for reading/writing high-throughput sequencing data. This package includes the utilities bgzip and tabix | |A C library for reading/writing high-throughput sequencing data. This package includes the utilities bgzip and tabix | ||
|- | |- | ||
− | |[https://github.com/biobakery/ | + | |[https://github.com/biobakery/humann/ humann] |
− | |3.0. | + | |3.0.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.1.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.6-IGB-gcc-8.2.0-Python-3.7.2<br>3.7-IGB-gcc-8.2.0-Python-3.7.2 |
|HUMAnN (the HMP Unified Metabolic Analysis Network) is a method for efficiently and accurately profiling the abundance of microbial metabolic pathways and other molecular functions from metagenomic or metatranscriptomic sequencing data. | |HUMAnN (the HMP Unified Metabolic Analysis Network) is a method for efficiently and accurately profiling the abundance of microbial metabolic pathways and other molecular functions from metagenomic or metatranscriptomic sequencing data. | ||
|- | |- | ||
Line 1,350: | Line 1,470: | ||
|- | |- | ||
|[https://www.ebi.ac.uk/interpro/interproscan.html InterProScan] | |[https://www.ebi.ac.uk/interpro/interproscan.html InterProScan] | ||
− | |5.27-66.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.28-67.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_201<br>5.47-82.0-IGB-gcc-8.2.0-Java-15.0.1 | + | |5.27-66.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.28-67.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_152<br>5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_201<br>5.47-82.0-IGB-gcc-8.2.0-Java-15.0.1<br>5.56-89.0-IGB-gcc-8.2.0-Java-15.0.1 |
|InterProScan is the software package that allows sequences (protein and nucleic) to be scanned against InterPros signatures. Signatures are predictive models, provided by several different databases, that make up the InterPro consortium. | |InterProScan is the software package that allows sequences (protein and nucleic) to be scanned against InterPros signatures. Signatures are predictive models, provided by several different databases, that make up the InterPro consortium. | ||
+ | |- | ||
+ | |[https://intervene.readthedocs.io/en/latest/ intervene] | ||
+ | |0.6.5 | ||
+ | |a tool for intersection and visualization of multiple genomic region sets | ||
|- | |- | ||
|[https://github.com/LLNL/ior ior] | |[https://github.com/LLNL/ior ior] | ||
Line 1,364: | Line 1,488: | ||
|5.3.0-IGB-gcc-4.9.4-Python-3.6.1 | |5.3.0-IGB-gcc-4.9.4-Python-3.6.1 | ||
|IPython provides a rich architecture for interactive computing with: Powerful interactive shells (terminal and Qt-based). A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. | |IPython provides a rich architecture for interactive computing with: Powerful interactive shells (terminal and Qt-based). A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. | ||
+ | |- | ||
+ | |[https://github.com/christophertbrown/iRep iRep] | ||
+ | |20191228-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |iRep is a method for determining replication rates for bacteria from single time point metagenomics sequencing and draft-quality genomes. | ||
+ | |- | ||
+ | |[https://github.com/williamritchie/IRFinder IRFinder] | ||
+ | |1.3.1-IGB-gcc-8.2.0 | ||
+ | |Detecting intron retention from RNA-Seq experiments | ||
+ | |- | ||
+ | |[https://github.com/PacificBiosciences/IsoSeq isoseq3] | ||
+ | |3.7.0-0 | ||
+ | |IsoSeq v3 contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq GUI-based analysis application. | ||
|- | |- | ||
|[https://microbiology.se/software/itsx/ ITSx] | |[https://microbiology.se/software/itsx/ ITSx] | ||
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| | | | ||
|Jansson is a C library for encoding, decoding and manipulating JSON data. | |Jansson is a C library for encoding, decoding and manipulating JSON data. | ||
+ | |- | ||
+ | |[https://github.com/mkirsche/Jasmine Jasmine] | ||
+ | |1.1.5 | ||
+ | |This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. | ||
|- | |- | ||
|[http://www.ece.uvic.ca/~frodo/jasper/ JasPer] | |[http://www.ece.uvic.ca/~frodo/jasper/ JasPer] | ||
Line 1,386: | Line 1,526: | ||
|- | |- | ||
|[http://java.com/ Java] | |[http://java.com/ Java] | ||
− | |1.8.0_121<br>1.8.0_152<br>1.8.0_201<br>11.0.5<br>15.0.1 | + | |1.8.0_121<br>1.8.0_152<br>1.8.0_201<br>11.0.5<br>15.0.1<br>17.0.6 |
− | |Java Platform, Standard Edition (Java SE) lets you develop and | + | |Java Platform, Standard Edition (Java SE) lets you develop and deployJava applications on desktops and servers. |
+ | |- | ||
+ | |[https://openjfx.io/ JavaFX] | ||
+ | |21 | ||
+ | |JavaFX is an open source, next generation client application platform for desktop, mobile and embedded systems built on Java. | ||
|- | |- | ||
|[https://github.com/ElArkk/jax-unirep jax-unirep] | |[https://github.com/ElArkk/jax-unirep jax-unirep] | ||
Line 1,394: | Line 1,538: | ||
|- | |- | ||
|[http://www.genome.umd.edu/jellyfish.html Jellyfish] | |[http://www.genome.umd.edu/jellyfish.html Jellyfish] | ||
− | |1.1.12-IGB-gcc-4.9.4<br>2.2.10-IGB-gcc-8.2.0<br>2.2.6-IGB-gcc-4.9.4<br>2.3.0-IGB-gcc-8.2.0 | + | |1.1.12-IGB-gcc-4.9.4<br>1.1.12-IGB-gcc-8.2.0<br>2.2.10-IGB-gcc-8.2.0<br>2.2.6-IGB-gcc-4.9.4<br>2.3.0-IGB-gcc-8.2.0 |
|Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. | |Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. | ||
|- | |- | ||
Line 1,426: | Line 1,570: | ||
|- | |- | ||
|[http://jupyter.org/ jupyterlab] | |[http://jupyter.org/ jupyterlab] | ||
− | | | + | |2.2.9-IGB-gcc-8.2.0-Python-3.7.2<br>3.5.0-IGB-gcc-8.2.0-Python-3.10.1 |
|Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. | |Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. | ||
|- | |- | ||
Line 1,442: | Line 1,586: | ||
|- | |- | ||
|[https://github.com/fchollet/keras Keras] | |[https://github.com/fchollet/keras Keras] | ||
− | |2.0.6-IGB-gcc-4.9.4-Python-2.7.13<br>2.0.8-IGB-gcc-4.9.4-Python-3.6.1<br>2.1.2-IGB-gcc-4.9.4-Python-3.6.1<br>2.1.5-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.2-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.4-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-4.9.4-Python-3.6.1-TF-2.0.3 | + | |2.0.6-IGB-gcc-4.9.4-Python-2.7.13<br>2.0.8-IGB-gcc-4.9.4-Python-3.6.1<br>2.1.2-IGB-gcc-4.9.4-Python-3.6.1<br>2.1.5-IGB-gcc-4.9.4-Python-3.6.1<br>2.11.0-IGB-gcc-8.2.0-Python-3.7.2<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.2-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.4-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-4.9.4-Python-3.6.1-TF-2.0.3 |
|Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. | |Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. | ||
|- | |- | ||
Line 1,448: | Line 1,592: | ||
|0.3.0-IGB-gcc-4.9.4-Python-2.7.13 | |0.3.0-IGB-gcc-4.9.4-Python-2.7.13 | ||
|keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. | |keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. | ||
+ | |- | ||
+ | |[https://www.kingrelatedness.com KING] | ||
+ | |2.3.2-IGB-gcc-8.2.0 | ||
+ | |KING is a toolset that makes use of high-throughput SNP data typically seen in a genome-wide association study (GWAS) or a sequencing project. Applications of KING include family relationship inference and pedigree error checking, quality control, population substructure identification, forensics, gene mapping, etc. | ||
|- | |- | ||
|[https://bitbucket.org/genomicepidemiology/kma kma] | |[https://bitbucket.org/genomicepidemiology/kma kma] | ||
Line 1,454: | Line 1,602: | ||
|- | |- | ||
|[https://github.com/refresh-bio/KMC KMC] | |[https://github.com/refresh-bio/KMC KMC] | ||
− | |3.1.1 | + | |3.1.1<br>3.2.1 |
|KMC is a disk-based programm for counting k-mers from (possibly gzipped) FASTQ/FASTA files. The homepage of the KMC project is http://sun.aei.polsl.pl/kmc | |KMC is a disk-based programm for counting k-mers from (possibly gzipped) FASTQ/FASTA files. The homepage of the KMC project is http://sun.aei.polsl.pl/kmc | ||
|- | |- | ||
Line 1,462: | Line 1,610: | ||
|- | |- | ||
|[https://bitbucket.org/biobakery/kneaddata/wiki/Home KneadData] | |[https://bitbucket.org/biobakery/kneaddata/wiki/Home KneadData] | ||
− | |0.10.0-IGB-gcc-8.2.0-Python-3.7.2<br>0.6.1-IGB-gcc-4.9.4-Python-3.6.1<br>0.8.0-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.10.0-IGB-gcc-8.2.0-Python-3.7.2<br>0.12.0-IGB-gcc-8.2.0-Python-3.7.2<br>0.6.1-IGB-gcc-4.9.4-Python-3.6.1<br>0.8.0-IGB-gcc-8.2.0-Python-3.7.2 |
|KneadData is a tool designed to perform quality control on metagenomic sequencing data, especially data from microbiome experiments. In these experiments, samples are typically taken from a host in hopes of learning something about the microbial community on the host. However, metagenomic sequencing data from such experiments will often contain a high ratio of host to bacterial reads. This tool aims to perform principled in silico separation of bacterial reads from these contaminant reads, be they from the host, from bacterial 16S sequences, or other user-defined sources. | |KneadData is a tool designed to perform quality control on metagenomic sequencing data, especially data from microbiome experiments. In these experiments, samples are typically taken from a host in hopes of learning something about the microbial community on the host. However, metagenomic sequencing data from such experiments will often contain a high ratio of host to bacterial reads. This tool aims to perform principled in silico separation of bacterial reads from these contaminant reads, be they from the host, from bacterial 16S sequences, or other user-defined sources. | ||
|- | |- | ||
|[https://ccb.jhu.edu/software/kraken/ Kraken] | |[https://ccb.jhu.edu/software/kraken/ Kraken] | ||
− | |1.0-IGB-gcc-4.9.4 | + | |1.0-IGB-gcc-4.9.4<br>1.1.1-IGB-gcc-8.2.0 |
|Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. | |Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. | ||
|- | |- | ||
|[https://ccb.jhu.edu/software/kraken/ Kraken2] | |[https://ccb.jhu.edu/software/kraken/ Kraken2] | ||
− | |2.0.8-beta-IGB-gcc-4.9.4<br>2.1.1-IGB-gcc-8.2.0 | + | |2.0.8-beta-IGB-gcc-4.9.4<br>2.1.1-IGB-gcc-8.2.0<br>2.1.2-IGB-gcc-8.2.0 |
|Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. | |Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. | ||
+ | |- | ||
+ | |[https://github.com/jenniferlu717/KrakenTools KrakenTools] | ||
+ | |1.2-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |KrakenTools is a suite of scripts to be used alongside the Kraken, KrakenUniq, Kraken 2, or Bracken programs. These scripts are designed to help Kraken users with downstream analysis of Kraken results. | ||
|- | |- | ||
|[https://github.com/marbl/Krona Krona] | |[https://github.com/marbl/Krona Krona] | ||
Line 1,504: | Line 1,656: | ||
|20180219-IGB-gcc-4.9.4-Python-2.7.13 | |20180219-IGB-gcc-4.9.4-Python-2.7.13 | ||
|LEfSe | |LEfSe | ||
+ | |- | ||
+ | |[https://sourceforge.net/p/lep-map3/wiki/LM3%20Home/ Lep-MAP3] | ||
+ | |20221128-Java-15.0.1 | ||
+ | |Lep-MAP3 (LM3) is a novel linkage map construction software suite. It can handle millions of markers and thousands of individuals possibly on multiple families. Input genotype data can be from genome sequencing (RADseq or whole genome sequencing), SNP assay, microsatellites or any mixture of them. | ||
|- | |- | ||
|[https://lftp.yar.ru/ lftp] | |[https://lftp.yar.ru/ lftp] | ||
− | |4.8. | + | |4.9.2-IGB-gcc-8.2.0 |
|FTP is a sophisticated file transfer program supporting a number of network protocols (ftp, http, sftp, fish, torrent). | |FTP is a sophisticated file transfer program supporting a number of network protocols (ftp, http, sftp, fish, torrent). | ||
+ | |- | ||
+ | |[https://gitlab.dkrz.de/k202009/libaec libaec] | ||
+ | |1.0.6-IGB-gcc-8.2.0 | ||
+ | |Libaec provides fast lossless compression of 1 up to 32 bit wide signed or unsigned integers(samples). The library achieves best results for low entropy data as often encountered in space imaginginstrument data or numerical model output from weather or climate simulations. While floating point representationsare not directly supported, they can also be efficiently coded by grouping exponents and mantissa. | ||
+ | |- | ||
+ | |libBigWig | ||
+ | | | ||
+ | |A C library for reading/parsing local and remote bigWig and bigBed files. While Kent's source code is free to use for these purposes | ||
|- | |- | ||
|libcerf | |libcerf | ||
Line 1,524: | Line 1,688: | ||
| | | | ||
|The libevent API provides a mechanism to execute a callback function when a specific event occurs on a file descriptor or after a timeout has been reached. Furthermore, libevent also support callbacks due to signals or regular timeouts. | |The libevent API provides a mechanism to execute a callback function when a specific event occurs on a file descriptor or after a timeout has been reached. Furthermore, libevent also support callbacks due to signals or regular timeouts. | ||
+ | |- | ||
+ | |libfaketime | ||
+ | | | ||
+ | |libfaketime intercepts various system calls that programs use to retrieve thecurrent date and time. It then reports modified (faked) dates and times (asspecified by you, the user) to these programs. This means you can modify thesystem time a program sees without having to change the time system-wide. | ||
|- | |- | ||
|libffi | |libffi | ||
Line 1,590: | Line 1,758: | ||
|- | |- | ||
|[https://www.nongnu.org/libunwind/ libunwind] | |[https://www.nongnu.org/libunwind/ libunwind] | ||
− | |1.3.1-IGB-gcc-4.9.4 | + | |1.3.1-IGB-gcc-4.9.4<br>1.5.0-IGB-gcc-8.2.0 |
|The primary goal of libunwind is to define a portable and efficient C programming interface (API) to determine the call-chain of a program. The API additionally provides the means to manipulate the preserved (callee-saved) state of each call-frame and to resume execution at any point in the call-chain (non-local goto). The API supports both local (same-process) and remote (across-process) operation. As such, the API is useful in a number of applications | |The primary goal of libunwind is to define a portable and efficient C programming interface (API) to determine the call-chain of a program. The API additionally provides the means to manipulate the preserved (callee-saved) state of each call-frame and to resume execution at any point in the call-chain (non-local goto). The API supports both local (same-process) and remote (across-process) operation. As such, the API is useful in a number of applications | ||
|- | |- | ||
Line 1,612: | Line 1,780: | ||
|1.6.1-IGB-gcc-8.2.0-Python-3.7.2 | |1.6.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. | |Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. | ||
+ | |- | ||
+ | |[https://lima.how/ lima] | ||
+ | |2.6.0-0 | ||
+ | |lima is the standard tool to identify barcode and primer sequences in PacBio single-molecule sequencing data. It powers the Demultiplex Barcodes, Iso-Seq, and Mark PCR Duplicates GUI-based analysis applications. | ||
|- | |- | ||
|[http://www.bcgsc.ca/platform/bioinfo/software/links LINKS] | |[http://www.bcgsc.ca/platform/bioinfo/software/links LINKS] | ||
Line 1,617: | Line 1,789: | ||
|LINKS is a scalable genomics application for scaffolding or re-scaffolding genome assembly drafts with long reads, such as those produced by Oxford Nanopore Technologies Ltd and Pacific Biosciences. It provides a generic alignment-free framework for scaffolding and can work on any sequences. It is versatile and supports not only long sequences as a source of long-range information, but also MPET pairs and linked-reads, such as those from the 10X Genomics GemCode and Chromium platform, via ARCS (http://www.bcgsc.ca/platform/bioinfo/software/arcs). Fill gaps in LINKS-derived scaffolds using Sealer (http://www.bcgsc.ca/platform/bioinfo/software/sealer). | |LINKS is a scalable genomics application for scaffolding or re-scaffolding genome assembly drafts with long reads, such as those produced by Oxford Nanopore Technologies Ltd and Pacific Biosciences. It provides a generic alignment-free framework for scaffolding and can work on any sequences. It is versatile and supports not only long sequences as a source of long-range information, but also MPET pairs and linked-reads, such as those from the 10X Genomics GemCode and Chromium platform, via ARCS (http://www.bcgsc.ca/platform/bioinfo/software/arcs). Fill gaps in LINKS-derived scaffolds using Sealer (http://www.bcgsc.ca/platform/bioinfo/software/sealer). | ||
|- | |- | ||
− | | | + | |LittleCMS |
− | | | + | | |
|Little CMS intends to be an OPEN SOURCE small-footprint color management engine, with special focus on accuracy and performance. - Homepage: http://www.littlecms.com/ | |Little CMS intends to be an OPEN SOURCE small-footprint color management engine, with special focus on accuracy and performance. - Homepage: http://www.littlecms.com/ | ||
|- | |- | ||
Line 1,636: | Line 1,808: | ||
|2.1.3<br>2.1.6<br>2.2.2 | |2.1.3<br>2.1.6<br>2.2.2 | ||
|Long Ranger is a set of analysis pipelines that processes Chromium sequencing output to align reads and call and phase SNPs, indels, and structural variants. There are five main pipelines, each triggered by a longranger command | |Long Ranger is a set of analysis pipelines that processes Chromium sequencing output to align reads and call and phase SNPs, indels, and structural variants. There are five main pipelines, each triggered by a longranger command | ||
+ | |- | ||
+ | |[https://github.com/LappalainenLab/lorals lorals] | ||
+ | |20210528-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |A Python package for allele-specific analyses in long-read data. | ||
|- | |- | ||
|[https://github.com/bcm-uga/Loter Loter] | |[https://github.com/bcm-uga/Loter Loter] | ||
Line 1,648: | Line 1,824: | ||
|5.5.2.5-IGB-gcc-4.9.4<br>5.5.2.5-IGB-gcc-8.2.0 | |5.5.2.5-IGB-gcc-4.9.4<br>5.5.2.5-IGB-gcc-8.2.0 | ||
|Mixed Integer Linear Programming (MILP) solver | |Mixed Integer Linear Programming (MILP) solver | ||
+ | |- | ||
+ | |[https://github.com/anuradhawick/LRBinner LRBinner] | ||
+ | |0.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |Binning Error-Prone Long Reads Using Auto Encoders | ||
|- | |- | ||
|[https://github.com/oushujun/LTR_retriever LTRretriever] | |[https://github.com/oushujun/LTR_retriever LTRretriever] | ||
Line 1,654: | Line 1,834: | ||
|- | |- | ||
|[http://www.lua.org/ Lua] | |[http://www.lua.org/ Lua] | ||
− | |5.1.5-IGB-gcc-4.9.4<br>5.3.4-IGB-gcc-4.9.4 | + | |5.1.5-IGB-gcc-4.9.4<br>5.1.5-IGB-gcc-8.2.0<br>5.3.4-IGB-gcc-4.9.4 |
|Lua is a powerful, fast, lightweight, embeddable scripting language. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode for a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. | |Lua is a powerful, fast, lightweight, embeddable scripting language. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode for a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. | ||
|- | |- | ||
Line 1,662: | Line 1,842: | ||
|- | |- | ||
|[https://lz4.github.io/lz4/ lz4] | |[https://lz4.github.io/lz4/ lz4] | ||
− | |1.9.2-IGB-gcc-4.9.4 | + | |1.9.2-IGB-gcc-4.9.4<br>1.9.2-IGB-gcc-8.2.0 |
|LZ4 is lossless compression algorithm, providing compression speed at 400 MB/s per core. It features an extremely fast decoder, with speed in multiple GB/s per core. | |LZ4 is lossless compression algorithm, providing compression speed at 400 MB/s per core. It features an extremely fast decoder, with speed in multiple GB/s per core. | ||
|- | |- | ||
Line 1,678: | Line 1,858: | ||
|- | |- | ||
|[https://github.com/taoliu/MACS/ MACS2] | |[https://github.com/taoliu/MACS/ MACS2] | ||
− | |2.1.1.20160309-IGB-gcc-4.9.4-Python-2.7.13<br>2.1.2-IGB-gcc-4.9.4-Python-2.7.13<br>2.2.5-IGB-gcc-4.9.4-Python-3.6.1 | + | |2.1.1.20160309-IGB-gcc-4.9.4-Python-2.7.13<br>2.1.2-IGB-gcc-4.9.4-Python-2.7.13<br>2.2.5-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.5-IGB-gcc-8.2.0-Python-3.7.2 |
|Model Based Analysis for ChIP-Seq data | |Model Based Analysis for ChIP-Seq data | ||
|- | |- | ||
|[http://mafft.cbrc.jp/alignment/software/ MAFFT] | |[http://mafft.cbrc.jp/alignment/software/ MAFFT] | ||
− | |7.310-IGB-gcc-4.9.4 | + | |7.310-IGB-gcc-4.9.4<br>7.490-IGB-gcc-8.2.0 |
− | |MAFFT is a multiple sequence alignment program for unix-like operating systems. It offers a range of multiple alignment methods, L-INS-i (accurate; for alignment of <∼200 sequences), FFT-NS-2 (fast; for alignment of <∼10,000 sequences), etc. | + | |MAFFT is a multiple sequence alignment program for unix-like operating systems. It offers a range of multiple alignment methods, L-INS-i (accurate; for alignment of <∼200 sequences), FFT-NS-2 (fast; for alignment of <∼10,000 sequences), etc. |
|- | |- | ||
|[https://bitbucket.org/liulab/mageck-vispr mageck-vispr] | |[https://bitbucket.org/liulab/mageck-vispr mageck-vispr] | ||
Line 1,700: | Line 1,880: | ||
|1.0.6-IGB-gcc-4.9.4-Python-3.6.1 | |1.0.6-IGB-gcc-4.9.4-Python-3.6.1 | ||
|A super-fast templating language that borrows the best ideas from the existing templating languages | |A super-fast templating language that borrows the best ideas from the existing templating languages | ||
+ | |- | ||
+ | |[https://github.com/Illumina/manta manta] | ||
+ | |1.6.0-IGB-gcc-8.2.0 | ||
+ | |Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. | ||
|- | |- | ||
|[https://github.com/ginolhac/mapDamage mapDamage] | |[https://github.com/ginolhac/mapDamage mapDamage] | ||
Line 1,752: | Line 1,936: | ||
|1.6.15.0-IGB-gcc-4.9.4<br>1.6.7.0-IGB-gcc-4.9.4 | |1.6.15.0-IGB-gcc-4.9.4<br>1.6.7.0-IGB-gcc-4.9.4 | ||
|MaxQuant is a proteomics software package designed for analyzing large mass-spectrometric data sets. It is specifically aimed at high-resolution MS data. | |MaxQuant is a proteomics software package designed for analyzing large mass-spectrometric data sets. It is specifically aimed at high-resolution MS data. | ||
+ | |- | ||
+ | |Mayo | ||
+ | |2024-IGB-gcc-8.2.0 | ||
+ | |Mayo Class Module | ||
+ | |- | ||
+ | |Mayo-Test | ||
+ | |2024-IGB-gcc-8.2.0 | ||
+ | |Mayo Class Module | ||
|- | |- | ||
|[https://github.com/mcveanlab/mccortex McCortex] | |[https://github.com/mcveanlab/mccortex McCortex] | ||
Line 1,758: | Line 1,950: | ||
|- | |- | ||
|[http://micans.org/mcl/ MCL] | |[http://micans.org/mcl/ MCL] | ||
− | |14.137-IGB-gcc-4.9.4 | + | |14.137-IGB-gcc-4.9.4<br>14.137-IGB-gcc-8.2.0 |
|The MCL algorithm is short for the Markov Cluster Algorithm, a fastand scalable unsupervised cluster algorithm for graphs (also known as networks) basedon simulation of (stochastic) flow in graphs. | |The MCL algorithm is short for the Markov Cluster Algorithm, a fastand scalable unsupervised cluster algorithm for graphs (also known as networks) basedon simulation of (stochastic) flow in graphs. | ||
+ | |- | ||
+ | |MCScanX | ||
+ | |20221031-IGB-gcc-8.2.0 | ||
+ | | | ||
|- | |- | ||
|[https://github.com/nanoporetech/medaka medaka] | |[https://github.com/nanoporetech/medaka medaka] | ||
Line 1,778: | Line 1,974: | ||
|- | |- | ||
|[http://meme-suite.org/ MEME] | |[http://meme-suite.org/ MEME] | ||
− | |4.11.2-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>4.12.0-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>5.0.1-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>5.0.5-IGB-gcc-4.9.4 | + | |4.11.2-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>4.12.0-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>5.0.1-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13<br>5.0.5-IGB-gcc-4.9.4<br>5.5.1-IGB-gcc-8.2.0 |
|The MEME Suite allows you to: * discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences, * search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN, * compare a motif to all motifs in a database of motifs, * associate motifs with Gene Ontology terms via their putative target genes, and * analyse motif enrichment using SpaMo or CentriMo. | |The MEME Suite allows you to: * discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences, * search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN, * compare a motif to all motifs in a database of motifs, * associate motifs with Gene Ontology terms via their putative target genes, and * analyse motif enrichment using SpaMo or CentriMo. | ||
|- | |- | ||
Line 1,790: | Line 1,986: | ||
|- | |- | ||
|[https://github.com/arangrhie/merfin Merfin] | |[https://github.com/arangrhie/merfin Merfin] | ||
− | |20210507-IGB-gcc-8.2.0 | + | |1.1-IGB-gcc-8.2.0<br>20210507-IGB-gcc-8.2.0 |
|k-mer-based assembly and variant calling evaluation for improved consensus accuracy. | |k-mer-based assembly and variant calling evaluation for improved consensus accuracy. | ||
+ | |- | ||
+ | |[https://github.com/Roy-lab/merlin-p merlin-p] | ||
+ | |20181020-IGB-gcc-8.2.0 | ||
+ | |Modular regulatory network learning with per gene information (MERLIN) is a network inference method that tries to infer a more accurate regulatory network by incorporating a modularity constraint. | ||
|- | |- | ||
|[https://github.com/marbl/merqury merqury] | |[https://github.com/marbl/merqury merqury] | ||
Line 1,820: | Line 2,020: | ||
|20080819-x86-64 | |20080819-x86-64 | ||
|MetaGeneAnnotator is a gene-finding program for prokaryote and phage. | |MetaGeneAnnotator is a gene-finding program for prokaryote and phage. | ||
+ | |- | ||
+ | |[https://github.com/gatech-genemark/MetaGeneMark-2 MetaGeneMark-2] | ||
+ | |20210406-IGB-gcc-8.2.0 | ||
+ | |MetaGeneMark-2 is an unsupervised metagenomic gene finder. It improves on MetaGeneMark by adding models for better gene start prediction, as well as automatic selection of genetic code (4 or 11). | ||
+ | |- | ||
+ | |[https://github.com/GaetanBenoitDev/metaMDBG metaMDBG] | ||
+ | |0.3 | ||
+ | |MetaMDBG is a fast and low-memory assembler for long and accurate metagenomics reads (e.g. PacBio HiFi). It is based on the minimizer de-Brujin graph (MDBG), which have been reimplemetend specifically for metagenomics assembly. | ||
|- | |- | ||
|[https://github.com/biobakery/MetaPhlAn/tree/3.0 metaphlan] | |[https://github.com/biobakery/MetaPhlAn/tree/3.0 metaphlan] | ||
− | |3.0.4-IGB-gcc-8.2.0-Python-3.7.2<br>3.0.7-IGB-gcc-8.2.0-Python-3.7.2 | + | |3.0.4-IGB-gcc-8.2.0-Python-3.7.2<br>3.0.7-IGB-gcc-8.2.0-Python-3.7.2<br>3.1.0-IGB-gcc-8.2.0-Python-3.7.2<br>4.0.0-IGB-gcc-8.2.0-Python-3.7.2<br>4.0.6-IGB-gcc-8.2.0-Python-3.7.2 |
|MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. | |MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. | ||
|- | |- | ||
Line 1,830: | Line 2,038: | ||
|- | |- | ||
|[https://www.agisoft.com metashape] | |[https://www.agisoft.com metashape] | ||
− | |1.7.1-IGB-gcc-8.2.0 | + | |1.7.1-IGB-gcc-8.2.0<br>2.0.1 |
+ | |Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales. | ||
+ | |- | ||
+ | |[https://www.agisoft.com metashape-pro] | ||
+ | |1.8.4-IGB-gcc-8.2.0<br>2.0.1<br>2.0.2 | ||
|Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales. | |Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales. | ||
+ | |- | ||
+ | |[https://www.agisoft.com metashape-python] | ||
+ | |1.7.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.0.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |Process digital images and generate 3D spatial data. Fast and highly accurate. | ||
|- | |- | ||
|[https://metavelvet.dna.bio.keio.ac.jp/ MetaVelvet] | |[https://metavelvet.dna.bio.keio.ac.jp/ MetaVelvet] | ||
Line 1,842: | Line 2,058: | ||
|- | |- | ||
|[https://github.com/bxlab/metaWRAP metaWRAP] | |[https://github.com/bxlab/metaWRAP metaWRAP] | ||
− | |1.2.3 | + | |1.2.3<br>1.3.2 |
|MetaWRAP also includes a novel bin reassembly module, which allows to drastically improve the quality of a set of bins by extracting the reads belonging to each bin, and reassembling the bins with a more permissive, non-metagenomic assembler. | |MetaWRAP also includes a novel bin reassembly module, which allows to drastically improve the quality of a set of bins by extracting the reads belonging to each bin, and reassembling the bins with a more permissive, non-metagenomic assembler. | ||
|- | |- | ||
Line 1,870: | Line 2,086: | ||
|- | |- | ||
|[https://www.continuum.io/anaconda-overview Miniconda3] | |[https://www.continuum.io/anaconda-overview Miniconda3] | ||
− | |4.10.3<br>4.7.12.1 | + | |23.5.2<br>4.10.3<br>4.7.12.1 |
|Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. | |Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. | ||
|- | |- | ||
Line 1,892: | Line 2,108: | ||
|2.19.0-IGB-gcc-8.2.0-Perl-5.28.1 | |2.19.0-IGB-gcc-8.2.0-Perl-5.28.1 | ||
|Scan contig files against traditional PubMLST typing schemes | |Scan contig files against traditional PubMLST typing schemes | ||
+ | |- | ||
+ | |[https://bioinformaticshome.com/tools/rna-seq/descriptions/mmquant.html mmquant] | ||
+ | |1.0.4-IGB-gcc-8.2.0 | ||
+ | |A tool to quantiy gene expression. The mmquant algorithm handles multiply mapping reads, i.e., duplicated genes by constructing merged genes. | ||
|- | |- | ||
|[https://github.com/soedinglab/MMseqs2 MMseqs2] | |[https://github.com/soedinglab/MMseqs2 MMseqs2] | ||
|10-6d92c | |10-6d92c | ||
|MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. | |MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. | ||
+ | |- | ||
+ | |[https://github.com/ddarriba/modeltest modeltest-ng] | ||
+ | |0.1.7 | ||
+ | |ModelTest-NG is a tool for selecting the best-fit model of evolution for DNA and protein alignments. ModelTest-NG supersedes jModelTest and ProtTest in one single tool, with graphical and command console interfaces. | ||
|- | |- | ||
|Mono | |Mono | ||
| | | | ||
|An open source, cross-platform, implementation of C# and the CLR that is binary compatible with Microsoft.NET. | |An open source, cross-platform, implementation of C# and the CLR that is binary compatible with Microsoft.NET. | ||
+ | |- | ||
+ | |[https://www.cs.helsinki.fi/group/pssmfind/ MOODS] | ||
+ | |1.9.4.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |MOODS is a suite of algorithms for matching position weight matrices (PWM) against DNA sequences. It features advanced matrix matching algorithms implemented in C++ that can be used to scan hundreds of matrices against chromosome-sized sequences in few seconds. | ||
|- | |- | ||
|[http://www.mothur.org/ Mothur] | |[http://www.mothur.org/ Mothur] | ||
− | |1.38.1.1<br>1.39.5<br>1.39.5-IGB-gcc-4.9.4<br>1.44.1-IGB-gcc-8.2.0 | + | |1.38.1.1<br>1.39.5<br>1.39.5-IGB-gcc-4.9.4<br>1.44.1-IGB-gcc-8.2.0<br>1.47.0-IGB-gcc-8.2.0 |
|Mothur is a single piece of open-source, expandable software to fill the bioinformatics needs of the microbial ecology community. | |Mothur is a single piece of open-source, expandable software to fill the bioinformatics needs of the microbial ecology community. | ||
|- | |- | ||
Line 1,934: | Line 2,162: | ||
|- | |- | ||
|[http://multiqc.info/ MultiQC] | |[http://multiqc.info/ MultiQC] | ||
− | |0.9-IGB-gcc-4.9.4-Python-2.7.13<br>1.11-IGB-gcc-8.2.0-Python-3.7.2<br>1.2-IGB-gcc-4.9.4-Python-2.7.13<br>1.6-IGB-gcc-4.9.4-Python-3.6.1<br>1.7-IGB-gcc-4.9.4-Python-3.6.1<br>1.7-IGB-gcc-8.2.0-Python-3.7.2<br>1.9-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.9-IGB-gcc-4.9.4-Python-2.7.13<br>1.11-IGB-gcc-8.2.0-Python-3.7.2<br>1.14-IGB-gcc-8.2.0-Python-3.7.2<br>1.15-IGB-gcc-8.2.0-Python-3.7.2<br>1.2-IGB-gcc-4.9.4-Python-2.7.13<br>1.6-IGB-gcc-4.9.4-Python-3.6.1<br>1.7-IGB-gcc-4.9.4-Python-3.6.1<br>1.7-IGB-gcc-8.2.0-Python-3.7.2<br>1.9-IGB-gcc-8.2.0-Python-3.7.2 |
|MultiQC searches a given directory for analysis logs and compiles a HTML report. Its a general use tool, perfect for summarising the output from numerous bioinformatics tools. | |MultiQC searches a given directory for analysis logs and compiles a HTML report. Its a general use tool, perfect for summarising the output from numerous bioinformatics tools. | ||
|- | |- | ||
|[http://mummer.sourceforge.net/ MUMmer] | |[http://mummer.sourceforge.net/ MUMmer] | ||
− | |3.23-IGB-gcc-4.9.4<br>4.0.0beta2-IGB-gcc-4.9.4<br>4.0.0beta2-IGB-gcc-8.2.0 | + | |3.23-IGB-gcc-4.9.4<br>3.23-IGB-gcc-8.2.0<br>4.0.0beta2-IGB-gcc-4.9.4<br>4.0.0beta2-IGB-gcc-8.2.0<br>4.0.0rc1-IGB-gcc-8.2.0 |
|MUMmer is a system for rapidly aligning entire genomes | |MUMmer is a system for rapidly aligning entire genomes | ||
|- | |- | ||
Line 1,968: | Line 2,196: | ||
| | | | ||
|NASM: General-purpose x86 assembler | |NASM: General-purpose x86 assembler | ||
+ | |- | ||
+ | |[https://www.ncbi.nlm.nih.gov/datasets/ ncbi-datasets] | ||
+ | |20220607<br>20221101<br>20240305 | ||
+ | |Install and use the NCBI Datasets command line tools | ||
|- | |- | ||
|[https://github.com/kblin/ncbi-genome-download ncbi-genome-download] | |[https://github.com/kblin/ncbi-genome-download ncbi-genome-download] | ||
Line 2,014: | Line 2,246: | ||
|- | |- | ||
|[https://www.nextflow.io/ nextflow] | |[https://www.nextflow.io/ nextflow] | ||
− | |0.25.7-Java-1.8.0_121<br>0.26.3-Java-1.8.0_152<br>18.10.1-Java-1.8.0_152<br>19.07.0-Java-1.8.0_152<br>20.01.0-Java-1.8.0_152<br>21.03.0-Java-1.8.0_152<br>21.03.0-Java-1.8. | + | |0.25.7-Java-1.8.0_121<br>0.26.3-Java-1.8.0_152<br>18.10.1-Java-1.8.0_152<br>19.07.0-Java-1.8.0_152<br>20.01.0-Java-1.8.0_152<br>21.03.0-Java-1.8.0_152<br>21.03.0-Java-1.8.0_201<br>21.04.1-Java-1.8.0_152<br>21.06.0-edge-Java-1.8.0_152<br>22.09.7-Java-11.0.5<br>22.10.1-Java-15.0.1<br>22.10.6-Java-15.0.1<br>23.10.0-Java-15.0.1 |
|Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. | |Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. | ||
|- | |- | ||
Line 2,022: | Line 2,254: | ||
|- | |- | ||
|[https://github.com/nf-core/tools nf-core] | |[https://github.com/nf-core/tools nf-core] | ||
− | |1.6-IGB-gcc-4.9.4-Python-3.6.1 | + | |1.6-IGB-gcc-4.9.4-Python-3.6.1<br>2.7.2-IGB-gcc-8.2.0-Python-3.10.1 |
− | |A | + | |A community effort to collect a curated set of analysis pipelines built using Nextflow. |
|- | |- | ||
|NGS | |NGS | ||
| | | | ||
|NGS is a new, domain-specific API for accessing reads, alignments and pileupsproduced from Next Generation Sequencing. | |NGS is a new, domain-specific API for accessing reads, alignments and pileupsproduced from Next Generation Sequencing. | ||
+ | |- | ||
+ | |[https://github.com/parklab/NGSCheckMate NGSCheckMate] | ||
+ | |20190507-IGB-gcc-8.2.0-Python-2.7.18 | ||
+ | |NGSCheckMate is a software package for identifying next generation sequencing (NGS) data files from the same individual. It analyzes various types of NGS data files including (but not limited to) whole genome sequencing (WGS), whole exome sequencing (WES), RNA-seq, ChIP-seq, and targeted sequencing of various depths. | ||
+ | |- | ||
+ | |[https://github.com/fgvieira/ngsF ngsF] | ||
+ | |1.2.0-IGB-gcc-8.2.0 | ||
+ | |ngsF is a program to estimate per-individual inbreeding coefficients under a probabilistic framework that takes the uncertainty of genotype's assignation into account. It avoids calling genotypes by using genotype likelihoods or posterior probabilities. | ||
+ | |- | ||
+ | |[https://github.com/fgvieira/ngsF-HMM ngsF-HMM] | ||
+ | |20200722-IGB-gcc-8.2.0 | ||
+ | |ngsF-HMM is a program to estimate per-individual inbreeding tracts using a two-state Hidden Markov Model (HMM). | ||
+ | |- | ||
+ | |[https://github.com/fgvieira/ngsLD ngsLD] | ||
+ | |1.2.0-IGB-gcc-8.2.0 | ||
+ | |ngsLD is a program to estimate pairwise linkage disequilibrium (LD) taking the uncertainty of genotype's assignation into account. It does so by avoiding genotype calling and using genotype likelihoods or posterior probabilities. | ||
|- | |- | ||
|[https://ninja-build.org/ ninja] | |[https://ninja-build.org/ ninja] | ||
Line 2,074: | Line 2,322: | ||
|- | |- | ||
|[http://numba.pydata.org/ numba] | |[http://numba.pydata.org/ numba] | ||
− | |0.34.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.34.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.35.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.52.0-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.34.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.34.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.35.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.52.0-IGB-gcc-8.2.0-Python-3.7.2<br>0.55.2-IGB-gcc-8.2.0-Python-3.7.2<br>0.59.0-IGB-gcc-8.2.0-Python-3.10.1 |
|Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. | |Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. | ||
+ | |- | ||
+ | |numpy | ||
+ | | | ||
+ | |The fundamental package for scientific computing with Python | ||
|- | |- | ||
|[http://www.nwchem-sw.org NWChem] | |[http://www.nwchem-sw.org NWChem] | ||
Line 2,098: | Line 2,350: | ||
|- | |- | ||
|[https://github.com/dovetail-genomics/Omni-C Omni-C] | |[https://github.com/dovetail-genomics/Omni-C Omni-C] | ||
− | |20210526-IGB-gcc-8.2.0 | + | |20210526-IGB-gcc-8.2.0-Python-3.7.2 |
|The Dovetail™ Omni-C™ library uses a sequence-independent endonuclease for chromatin digestion prior to proximity ligation and library generation. | |The Dovetail™ Omni-C™ library uses a sequence-independent endonuclease for chromatin digestion prior to proximity ligation and library generation. | ||
|- | |- | ||
Line 2,118: | Line 2,370: | ||
|- | |- | ||
|[http://www.open-mpi.org/ OpenMPI] | |[http://www.open-mpi.org/ OpenMPI] | ||
− | |2.1.0-GCC-4.9.4-2.28<br>4.0.0-GCC-8.2.0-2.32 | + | |2.1.0-GCC-4.9.4-2.28<br>4.0.0-GCC-8.2.0-2.32 |
|The Open MPI Project is an open source MPI-3 implementation. | |The Open MPI Project is an open source MPI-3 implementation. | ||
|- | |- | ||
Line 2,124: | Line 2,376: | ||
| | | | ||
|OpenPGM is an open source implementation of the Pragmatic General Multicast (PGM) specification in RFC 3208 available at www.ietf.org. PGM is a reliable and scalable multicast protocol that enables receivers to detect loss, request retransmission of lost data, or notify an application of unrecoverable loss. PGM is a receiver-reliable protocol, which means the receiver is responsible for ensuring all data is received, absolving the sender of reception responsibility. | |OpenPGM is an open source implementation of the Pragmatic General Multicast (PGM) specification in RFC 3208 available at www.ietf.org. PGM is a reliable and scalable multicast protocol that enables receivers to detect loss, request retransmission of lost data, or notify an application of unrecoverable loss. PGM is a receiver-reliable protocol, which means the receiver is responsible for ensuring all data is received, absolving the sender of reception responsibility. | ||
+ | |- | ||
+ | |[https://opensfm.org/ OpenSfM] | ||
+ | |0.5.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |OpenSfM is a Structure from Motion library written in Python. The library serves as a processing pipeline for reconstructing camera poses and 3D scenes from multiple images. | ||
|- | |- | ||
|[http://www.openssl.org/ OpenSSL] | |[http://www.openssl.org/ OpenSSL] | ||
Line 2,130: | Line 2,386: | ||
|- | |- | ||
|[https://github.com/davidemms/OrthoFinder OrthoFinder] | |[https://github.com/davidemms/OrthoFinder OrthoFinder] | ||
− | |2.2.7<br>2.3.7-IGB-gcc-4.9.4 | + | |2.2.7<br>2.3.7-IGB-gcc-4.9.4<br>2.5.4-IGB-gcc-8.2.0 |
|OrthoFinder is a fast, accurate and comprehensive platform for comparative genomics. It finds orthologs and orthogroups, infers rooted gene trees for all orthogroups and identifies all of the gene duplcation events in those gene trees. | |OrthoFinder is a fast, accurate and comprehensive platform for comparative genomics. It finds orthologs and orthogroups, infers rooted gene trees for all orthogroups and identifies all of the gene duplcation events in those gene trees. | ||
|- | |- | ||
Line 2,136: | Line 2,392: | ||
|2.0.9-IGB-gcc-4.9.4-Perl-5.24.1 | |2.0.9-IGB-gcc-4.9.4-Perl-5.24.1 | ||
|OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. - Homepage: http://orthomcl.org/ | |OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. - Homepage: http://orthomcl.org/ | ||
+ | |- | ||
+ | |[https://github.com/jacirrone/OutPredict OutPredict] | ||
+ | |1.0.0-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |This repository contains OutPredict, a python developed Method for Predicting Out-of-sample Data in Time Series and Steady State data as well as to predict Causal connections from transcription factors to genes. | ||
|- | |- | ||
|[https://github.com/jinfeihan57/p7zip/ p7zip] | |[https://github.com/jinfeihan57/p7zip/ p7zip] | ||
Line 2,158: | Line 2,418: | ||
|- | |- | ||
|[https://github.com/mirnylab/pairtools pairtools] | |[https://github.com/mirnylab/pairtools pairtools] | ||
− | |0.3.0-IGB-gcc-4.9.4-Python-3.6.1 | + | |0.3.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.0.2-IGB-gcc-8.2.0-Python-3.7.2 |
|pairtools is a simple and fast command-line framework to process sequencing data from a Hi-C experiment. | |pairtools is a simple and fast command-line framework to process sequencing data from a Hi-C experiment. | ||
|- | |- | ||
Line 2,196: | Line 2,456: | ||
|0.0.6<br>0.0.8 | |0.0.6<br>0.0.8 | ||
|PacBio Assembly Tool Suite | |PacBio Assembly Tool Suite | ||
+ | |- | ||
+ | |[https://github.com/PacificBiosciences/pbAA pbaa] | ||
+ | |1.0.3.0 | ||
+ | |PacBio Amplicon Analysis (pbaa) separates complex mixtures of amplicon targets from genomic samples. The pbaa application is designed to cluster and generate high-quality consensus sequences from HiFi reads. | ||
|- | |- | ||
|[https://github.com/PacificBiosciences/pbbam pbbam] | |[https://github.com/PacificBiosciences/pbbam pbbam] | ||
Line 2,202: | Line 2,466: | ||
|- | |- | ||
|[https://github.com/PacificBiosciences/ccs pbccs] | |[https://github.com/PacificBiosciences/ccs pbccs] | ||
− | |4.0 | + | |4.0<br>6.4.0 |
|ccs takes multiple subreads of the same SMRTbell molecule and combines them using a statistical model to produce one highly accurate consensus sequence, also called HiFi read, with base quality values. | |ccs takes multiple subreads of the same SMRTbell molecule and combines them using a statistical model to produce one highly accurate consensus sequence, also called HiFi read, with base quality values. | ||
|- | |- | ||
Line 2,210: | Line 2,474: | ||
|- | |- | ||
|[https://github.com/PacificBiosciences/pbmm2 pbmm2] | |[https://github.com/PacificBiosciences/pbmm2 pbmm2] | ||
− | |1.4.0 | + | |1.12.0<br>1.4.0 |
|pbmm2 is a SMRT C++ wrapper for minimap2's C API. Its purpose is to support native PacBio in- and output, provide sets of recommended parameters, generate sorted output on-the-fly, and postprocess alignments. | |pbmm2 is a SMRT C++ wrapper for minimap2's C API. Its purpose is to support native PacBio in- and output, provide sets of recommended parameters, generate sorted output on-the-fly, and postprocess alignments. | ||
|- | |- | ||
Line 2,218: | Line 2,482: | ||
|- | |- | ||
|[https://github.com/Rosemeis/pcangsd/ pcangsd] | |[https://github.com/Rosemeis/pcangsd/ pcangsd] | ||
− | |0.9-IGB-gcc-4.9.4-Python-2.7.13 | + | |0.9-IGB-gcc-4.9.4-Python-2.7.13<br>20220330-IGB-gcc-8.2.0-Python-3.7.2 |
− | |Framework for analyzing low depth next-generation sequencing (NGS) data in heterogeneous populations using principal component analysis (PCA). | + | |Framework for analyzing low-depth next-generation sequencing (NGS) data in heterogeneous/structured populations using principal component analysis (PCA). |
|- | |- | ||
|PCRE | |PCRE | ||
Line 2,255: | Line 2,519: | ||
|[http://www.perl.org/ Perl] | |[http://www.perl.org/ Perl] | ||
|5.24.1-IGB-gcc-4.9.4<br>5.24.1-IGB-gcc-4.9.4-bare<br>5.26.1-IGB-gcc-4.9.4-unthreaded<br>5.28.1-IGB-gcc-8.2.0 | |5.24.1-IGB-gcc-4.9.4<br>5.24.1-IGB-gcc-4.9.4-bare<br>5.26.1-IGB-gcc-4.9.4-unthreaded<br>5.28.1-IGB-gcc-8.2.0 | ||
− | |Larry Wall Practical Extraction and Report Language | + | |Larry Wall's Practical Extraction and Report Language |
|- | |- | ||
|[ftp://ftp.ebi.ac.uk/pub/databases/Pfam/Tools/README pfamscan] | |[ftp://ftp.ebi.ac.uk/pub/databases/Pfam/Tools/README pfamscan] | ||
|1.6-IGB-gcc-4.9.4-Perl-5.24.1 | |1.6-IGB-gcc-4.9.4-Perl-5.24.1 | ||
|This readme should help you get started with "pfam_scan.pl", which is for usewith the HMMER3 version of HMMER. | |This readme should help you get started with "pfam_scan.pl", which is for usewith the HMMER3 version of HMMER. | ||
+ | |- | ||
+ | |[http://bioinfadmin.cs.ucl.ac.uk/downloads/pfilt/ pfilt] | ||
+ | |1.5-IGB-gcc-8.2.0 | ||
+ | |The pfilt program is designed to mask out (i.e. replace amino acid characterswith Xs) regions of low complexity, coiled-coil regions and more regions withextremely biased amino acid compositions. | ||
+ | |- | ||
+ | |[https://github.com/sib-swiss/pftools3 pftoolsV3] | ||
+ | |3.2.12-IGB-gcc-8.2.0 | ||
+ | |A suite of tools to build and search generalized profiles (protein and DNA). | ||
|- | |- | ||
|[http://www.cmpg.unibe.ch/software/PGDSpider/ PGDSpider] | |[http://www.cmpg.unibe.ch/software/PGDSpider/ PGDSpider] | ||
Line 2,294: | Line 2,566: | ||
|- | |- | ||
|[https://github.com/biobakery/phylophlan phylophlan] | |[https://github.com/biobakery/phylophlan phylophlan] | ||
− | |3.0.1-IGB-gcc-8.2.0-Python-3.7.2 | + | |3.0.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.0.3-IGB-gcc-8.2.0-Python-3.7.2 |
|PhyloPhlAn 3.0 is an integrated pipeline for large-scale phylogenetic profiling of genomes and metagenomes. | |PhyloPhlAn 3.0 is an integrated pipeline for large-scale phylogenetic profiling of genomes and metagenomes. | ||
|- | |- | ||
Line 2,306: | Line 2,578: | ||
|- | |- | ||
|[http://broadinstitute.github.io/picard/ picard] | |[http://broadinstitute.github.io/picard/ picard] | ||
− | |1.77-Java-1.8.0_152<br>2.10.1-Java-1.8. | + | |1.77-Java-1.8.0_152<br>2.10.1-Java-1.8.0_152<br>2.27.5-Java-1.8.0_201<br>2.9.0-Java-1.8.0_121<br>2.9.4-Java-1.8.0_121 |
|A set of tools (in Java) for working with next generation sequencing data in the BAM (http://samtools.github.io/hts-specs) format. | |A set of tools (in Java) for working with next generation sequencing data in the BAM (http://samtools.github.io/hts-specs) format. | ||
|- | |- | ||
Line 2,364: | Line 2,636: | ||
|1.07<br>1.90 | |1.07<br>1.90 | ||
|This is a comprehensive update to Shaun Purcells PLINK command-line program, developed by Christopher Chang with support from the NIH-NIDDKs Laboratory of Biological Modeling, the Purcell Lab at Mount Sinai School of Medicine, and others. | |This is a comprehensive update to Shaun Purcells PLINK command-line program, developed by Christopher Chang with support from the NIH-NIDDKs Laboratory of Biological Modeling, the Purcell Lab at Mount Sinai School of Medicine, and others. | ||
+ | |- | ||
+ | |[https://github.com/schneebergerlab/plotsr plotsr] | ||
+ | |1.1.1-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |Plotsr generates high-quality visualisation of synteny and structural rearrangements between multiple genomes. For this, it uses the genomic structural annotations between multiple chromosome-level assemblies. | ||
|- | |- | ||
|[https://github.com/nanoporetech/pomoxis pomoxis] | |[https://github.com/nanoporetech/pomoxis pomoxis] | ||
|0.1.0-IGB-gcc-4.9.4-Python-3.6.1 | |0.1.0-IGB-gcc-4.9.4-Python-3.6.1 | ||
|Pomoxis contains a set of services to perform analysis of squiggles as they are produced in real-time along with fast pipelines for generating draft assemblies. | |Pomoxis contains a set of services to perform analysis of squiggles as they are produced in real-time along with fast pipelines for generating draft assemblies. | ||
+ | |- | ||
+ | |[https://github.com/bkehr/popins popins] | ||
+ | |1.0.1-IGB-gcc-8.2.0 | ||
+ | |Population-scale detection of novel-sequence insertions. | ||
+ | |- | ||
+ | |[https://github.com/kehrlab/PopIns2 popins2] | ||
+ | |20220127-IGB-gcc-8.2.0 | ||
+ | |Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs | ||
+ | |- | ||
+ | |[https://sourceforge.net/p/popoolation2/wiki/Main/ popoolation2] | ||
+ | |1201-IGB-gcc-8.2.0-Perl-5.28.1 | ||
+ | |PoPoolation2 allows to compare allele frequencies for SNPs between two or more populations and to identify significant differences. | ||
|- | |- | ||
|poppler | |poppler | ||
Line 2,418: | Line 2,706: | ||
|- | |- | ||
|[https://github.com/tseemann/prokka prokka] | |[https://github.com/tseemann/prokka prokka] | ||
− | |1.13-IGB-gcc-4.9.4-Perl-5.24.1<br>1.14.6-IGB-gcc-4.9.4-Perl-5.24.1 | + | |1.13-IGB-gcc-4.9.4-Perl-5.24.1<br>1.14.6-IGB-gcc-4.9.4-Perl-5.24.1<br>1.14.6-IGB-gcc-8.2.0-Perl-5.28.1 |
|Whole genome annotation is the process of identifying features of interest in a set of genomic DNA sequences, and labelling them with useful information. Prokka is a software tool to annotate bacterial, archaeal and viral genomes quickly and produce standards-compliant output files. | |Whole genome annotation is the process of identifying features of interest in a set of genomic DNA sequences, and labelling them with useful information. Prokka is a software tool to annotate bacterial, archaeal and viral genomes quickly and produce standards-compliant output files. | ||
|- | |- | ||
Line 2,424: | Line 2,712: | ||
|0.7.45-IGB-gcc-4.9.4-Python-2.7.13 | |0.7.45-IGB-gcc-4.9.4-Python-2.7.13 | ||
|Prost (PRocessing Of Small Transcripts) is a python application that quantifies and annotates microRNA (miRNA) expression in metazoans with assembled genomes. Prost works by counting short transcripts within a user-specifiable length range. These counted transcripts are aligned to a user specifiable genome allowing for post-transcriptional modification (e.g. untemplated additions, editing, alternative cutting) and then "binned" together based on genomic location. Each bin is then annotated with databases of mature miRNAs, hairpins, and other types of RNAs (the databases may be derived from miRBase, Ensembls BioMart, other databases, or may be custom built by the user). | |Prost (PRocessing Of Small Transcripts) is a python application that quantifies and annotates microRNA (miRNA) expression in metazoans with assembled genomes. Prost works by counting short transcripts within a user-specifiable length range. These counted transcripts are aligned to a user specifiable genome allowing for post-transcriptional modification (e.g. untemplated additions, editing, alternative cutting) and then "binned" together based on genomic location. Each bin is then annotated with databases of mature miRNAs, hairpins, and other types of RNAs (the databases may be derived from miRBase, Ensembls BioMart, other databases, or may be custom built by the user). | ||
+ | |- | ||
+ | |[https://github.com/google-research/proteinfer proteinfer] | ||
+ | |20220411-IGB-gcc-4.9.4-Python-3.6.1 | ||
+ | |ProteInfer is an approach for predicting the functional properties of protein sequences using deep neural networks. | ||
|- | |- | ||
|[https://github.com/gatech-genemark/ProtHint ProtHint] | |[https://github.com/gatech-genemark/ProtHint ProtHint] | ||
Line 2,430: | Line 2,722: | ||
|- | |- | ||
|[https://github.com/google/protobuf protobuf] | |[https://github.com/google/protobuf protobuf] | ||
− | |2.6.1-IGB-gcc-4.9.4<br>3.5.0-IGB-gcc-4.9.4 | + | |2.6.1-IGB-gcc-4.9.4<br>23.4-IGB-gcc-8.2.0<br>3.5.0-IGB-gcc-4.9.4 |
|Protocol Buffers (a.k.a., protobuf) are Googles language-neutral, platform-neutral, extensible mechanism for serializing structured data. | |Protocol Buffers (a.k.a., protobuf) are Googles language-neutral, platform-neutral, extensible mechanism for serializing structured data. | ||
+ | |- | ||
+ | |[https://github.com/google/protobuf/ protobuf-python] | ||
+ | |3.19.4-IGB-gcc-8.2.0 | ||
+ | |Python Protocol Buffers runtime library. | ||
+ | |- | ||
+ | |[https://github.com/ddarriba/prottest3 prottest3] | ||
+ | |3.4.2 | ||
+ | |ProtTest is a bioinformatic tool for the selection of best-fit models of aminoacid replacement for the data at hand. ProtTest makes this selection by finding the model in the candidate list with the smallest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) score or Decision Theory Criterion (DT). | ||
+ | |- | ||
+ | |[https://github.com/psipred/psipred psipred] | ||
+ | |4.0-IGB-gcc-8.2.0<br>4.02-IGB-gcc-8.2.0 | ||
+ | |The PSIPRED Workbench provides a range of protein structure prediction methods. The site can be used interactively via a web browser or programmatically via our REST API. For high-throughput analyses, downloads of all the algorithms are available. | ||
|- | |- | ||
|[https://github.com/lilydjwg/pssh pssh] | |[https://github.com/lilydjwg/pssh pssh] | ||
Line 2,458: | Line 2,762: | ||
|- | |- | ||
|[https://daler.github.io/pybedtools/main.html pybedtools] | |[https://daler.github.io/pybedtools/main.html pybedtools] | ||
− | |0.7.10-IGB-gcc-4.9.4-Python-3.6.1 | + | |0.7.10-IGB-gcc-4.9.4-Python-3.6.1<br>0.8.2-IGB-gcc-8.2.0-Python-3.7.2<br>0.9.0-IGB-gcc-8.2.0-Python-3.7.2 |
|pybedtools is a Python package that wraps BEDTools, so you will need both installed. | |pybedtools is a Python package that wraps BEDTools, so you will need both installed. | ||
+ | |- | ||
+ | |[https://pybind11.readthedocs.io pybind11] | ||
+ | |2.9.2-IGB-gcc-8.2.0 | ||
+ | |pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. | ||
+ | |- | ||
+ | |[https://pycairo.readthedocs.io/en/latest/ pycairo] | ||
+ | |1.19.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |Pycairo is a Python module providing bindings for the cairo graphics library. It depends on cairo >= 1.15.10 and works with Python 3.7+. Pycairo, including this documentation, is licensed under the LGPL-2.1-only OR MPL-1.1. | ||
|- | |- | ||
|[https://mathema.tician.de/software/pycuda/ PyCUDA] | |[https://mathema.tician.de/software/pycuda/ PyCUDA] | ||
|2017.1-IGB-gcc-4.9.4-Python-2.7.13 | |2017.1-IGB-gcc-4.9.4-Python-2.7.13 | ||
|PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. - Homepage: https://mathema.tician.de/software/pycuda/ | |PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. - Homepage: https://mathema.tician.de/software/pycuda/ | ||
+ | |- | ||
+ | |[https://mathema.tician.de/software/pycuda/ pyCUDA] | ||
+ | |2024.1-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |PyCUDA lets you access Nvidia’s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist-so what’s so special about PyCUDA? | ||
|- | |- | ||
|[https://github.com/brentp/pyfasta/ pyfasta] | |[https://github.com/brentp/pyfasta/ pyfasta] | ||
|0.5.2-IGB-gcc-4.9.4-Python-2.7.13 | |0.5.2-IGB-gcc-4.9.4-Python-2.7.13 | ||
|Stores a flattened version of the fasta file without spaces or headers and uses either a mmap of numpy binary format or fseek/fread so the sequence data is never read into memory. | |Stores a flattened version of the fasta file without spaces or headers and uses either a mmap of numpy binary format or fseek/fread so the sequence data is never read into memory. | ||
+ | |- | ||
+ | |[https://github.com/deeptools/pyGenomeTracks pyGenomeTracks] | ||
+ | |3.8-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. | ||
|- | |- | ||
|[http://deeplearning.net/software/libgpuarray pygpu] | |[http://deeplearning.net/software/libgpuarray pygpu] | ||
Line 2,494: | Line 2,814: | ||
|- | |- | ||
|[http://python.org/ Python] | |[http://python.org/ Python] | ||
− | |2.7.13-IGB-gcc-4.9.4<br>3.10.1-IGB-gcc-8.2.0<br>3.6.1-IGB-gcc-4.9.4<br>3.7.2-IGB-gcc-8.2.0 | + | |2.7.13-IGB-gcc-4.9.4<br>2.7.18-IGB-gcc-8.2.0<br>3.10.1-IGB-gcc-8.2.0<br>3.6.1-IGB-gcc-4.9.4<br>3.7.2-IGB-gcc-8.2.0 |
|Python is a programming language that lets you work more quickly and integrate your systems more effectively. | |Python is a programming language that lets you work more quickly and integrate your systems more effectively. | ||
|- | |- | ||
|[http://pytorch.org PyTorch] | |[http://pytorch.org PyTorch] | ||
− | |0.3.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.4.1-IGB-gcc-4.9.4-Python-2.7.13<br>1.0.1.post2-IGB-gcc-4.9.4-Python-3.6.1<br>1.3.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.6.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.7.0-IGB-gcc-8.2.0-Python-3.7.2<br>1.9.0-IGB-gcc-8.2.0-Python-3.7.2 | + | |0.3.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>0.4.1-IGB-gcc-4.9.4-Python-2.7.13<br>1.0.1.post2-IGB-gcc-4.9.4-Python-3.6.1<br>1.11.0-IGB-gcc-8.2.0-Python-3.7.2<br>1.12.1-IGB-gcc-8.2.0-Python-3.10.1<br>1.12.1-IGB-gcc-8.2.0-Python-3.7.2<br>1.13.1-IGB-gcc-8.2.0-Python-3.10.1<br>1.13.1-IGB-gcc-8.2.0-Python-3.7.2<br>1.3.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.4.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.6.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.7.0-IGB-gcc-8.2.0-Python-3.7.2<br>1.9.0-IGB-gcc-8.2.0-Python-3.7.2 |
|Tensors and Dynamic neural networks in Pythonwith strong GPU acceleration. | |Tensors and Dynamic neural networks in Pythonwith strong GPU acceleration. | ||
+ | |- | ||
+ | |[https://github.com/pyg-team/pytorch_geometric pytorch-geometric] | ||
+ | |2.0.4-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. | ||
|- | |- | ||
|[http://www.zeromq.org/bindings:python PyZMQ] | |[http://www.zeromq.org/bindings:python PyZMQ] | ||
Line 2,514: | Line 2,838: | ||
|- | |- | ||
|[http://qiime.org/ QIIME2] | |[http://qiime.org/ QIIME2] | ||
− | |2017.10<br>2017.11<br>2017.12<br>2017.6<br>2017.8<br>2018.11<br>2018.2<br>2018.6<br>2018.8<br>2019.10<br>2019.4<br>2019.7<br>2020.2<br>2020. | + | |2017.10<br>2017.11<br>2017.12<br>2017.6<br>2017.8<br>2018.11<br>2018.2<br>2018.6<br>2018.8<br>2019.10<br>2019.4<br>2019.7<br>2020.2<br>2020.6<br>2020.8<br>2021.4<br>2022.8<br>2023.2<br>2023.7 |
|QIIME is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. | |QIIME is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. | ||
|- | |- | ||
|[http://qt.io/ Qt] | |[http://qt.io/ Qt] | ||
− | |4.8.7-IGB-gcc-4.9.4 | + | |4.8.7-IGB-gcc-4.9.4<br>4.8.7-IGB-gcc-8.2.0 |
|Qt is a comprehensive cross-platform C++ application framework. | |Qt is a comprehensive cross-platform C++ application framework. | ||
|- | |- | ||
Line 2,534: | Line 2,858: | ||
|- | |- | ||
|[http://www.r-project.org/ R] | |[http://www.r-project.org/ R] | ||
− | |2.15.3-IGB-gcc-4.9.4<br>3.1.2-IGB-gcc-4.9.4<br>3.2.5-IGB-gcc-4.9.4<br>3.3.3-IGB-gcc-4.9.4<br>3.4.1-IGB-gcc-4.9.4<br>3.4.2-IGB-gcc-4.9.4<br>3.5.0-IGB-gcc-4.9.4<br>3.6.0-IGB-gcc-8.2.0<br>4.0.3-IGB-gcc-8.2.0<br>4.1.2-IGB-gcc-8.2.0 | + | |2.15.3-IGB-gcc-4.9.4<br>3.1.2-IGB-gcc-4.9.4<br>3.2.5-IGB-gcc-4.9.4<br>3.3.3-IGB-gcc-4.9.4<br>3.4.1-IGB-gcc-4.9.4<br>3.4.2-IGB-gcc-4.9.4<br>3.5.0-IGB-gcc-4.9.4<br>3.6.0-IGB-gcc-8.2.0<br>4.0.3-IGB-gcc-8.2.0<br>4.1.2-IGB-gcc-8.2.0<br>4.2.2-IGB-gcc-8.2.0<br>4.3.2-IGB-gcc-8.2.0<br>4.4.0-IGB-gcc-8.2.0 |
|R is a free software environment for statistical computing and graphics. | |R is a free software environment for statistical computing and graphics. | ||
|- | |- | ||
Line 2,540: | Line 2,864: | ||
|0.5.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.5.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.4.13-IGB-gcc-8.2.0 | |0.5.0-IGB-gcc-4.9.4-Python-2.7.13<br>0.5.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.4.13-IGB-gcc-8.2.0 | ||
|Racon is intended as a standalone consensus module to correct raw contigs generated by rapid assembly methods which do not include a consensus step, such as Miniasm.The goal of Racon is to generate genomic consensus which is of similar or better quality compared to the output generated by assembly methods which employ both error correction and consensus steps, while providing a speedup of several times compared to those methods. | |Racon is intended as a standalone consensus module to correct raw contigs generated by rapid assembly methods which do not include a consensus step, such as Miniasm.The goal of Racon is to generate genomic consensus which is of similar or better quality compared to the output generated by assembly methods which employ both error correction and consensus steps, while providing a speedup of several times compared to those methods. | ||
+ | |- | ||
+ | |[https://github.com/AndreasHeger/radar radar] | ||
+ | |1.3-IGB-gcc-8.2.0 | ||
+ | |Welcome to radar | ||
|- | |- | ||
|[http://catchenlab.life.illinois.edu/radinitio/ radinitio] | |[http://catchenlab.life.illinois.edu/radinitio/ radinitio] | ||
Line 2,564: | Line 2,892: | ||
|8.2.12-IGB-gcc-4.9.4 | |8.2.12-IGB-gcc-4.9.4 | ||
|RAxML search algorithm for maximum likelihood based inference of phylogenetic trees. | |RAxML search algorithm for maximum likelihood based inference of phylogenetic trees. | ||
+ | |- | ||
+ | |[https://github.com/amkozlov/raxml-ng raxml-ng] | ||
+ | |1.2.0 | ||
+ | |RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion. Its search heuristic is based on iteratively performing a series of Subtree Pruning and Regrafting (SPR) moves, which allows to quickly navigate to the best-known ML tree. | ||
|- | |- | ||
|[https://rclone.org/ rclone] | |[https://rclone.org/ rclone] | ||
− | |1.41<br>1.52.3 | + | |1.41<br>1.52.3<br>1.60.0 |
|Rclone is a command line program to sync files and directories to and from different cloud storage | |Rclone is a command line program to sync files and directories to and from different cloud storage | ||
|- | |- | ||
Line 2,584: | Line 2,916: | ||
|1.0.1-IGB-gcc-8.2.0-Python-3.7.2 | |1.0.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|READemption is a pipeline for the computational evaluation of RNA-Seq data. It was originally developed to process dRNA-Seq reads (as introduced by Sharma et al., Nature, 2010 (Pubmed)) originating from bacterial samples. | |READemption is a pipeline for the computational evaluation of RNA-Seq data. It was originally developed to process dRNA-Seq reads (as introduced by Sharma et al., Nature, 2010 (Pubmed)) originating from bacterial samples. | ||
+ | |- | ||
+ | |[https://faculty.washington.edu/tathornt/software/REAP/index.html REAP] | ||
+ | |1.2-IGB-gcc-8.2.0 | ||
+ | | | ||
|- | |- | ||
|[http://www.sanger.ac.uk/science/tools/reapr REAPR] | |[http://www.sanger.ac.uk/science/tools/reapr REAPR] | ||
Line 2,598: | Line 2,934: | ||
|- | |- | ||
|[http://www.repeatmasker.org/ RepeatMasker] | |[http://www.repeatmasker.org/ RepeatMasker] | ||
− | |4.0.7-IGB-gcc-4.9.4-Perl-5.24.1<br>4.0.7-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded<br>4.1.1-IGB-gcc-4.9.4-Perl-5.24.1<br>4.1.2-p1-IGB-gcc-8.2.0-Perl-5.28.1 | + | |4.0.7-IGB-gcc-4.9.4-Perl-5.24.1<br>4.0.7-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded<br>4.1.1-IGB-gcc-4.9.4-Perl-5.24.1<br>4.1.2-p1-IGB-gcc-8.2.0-Perl-5.28.1<br>4.1.6-IGB-gcc-8.2.0-Perl-5.28.1 |
|RepeatMasker is a program that screens DNA sequences for interspersed repeats and low complexity DNA sequences. | |RepeatMasker is a program that screens DNA sequences for interspersed repeats and low complexity DNA sequences. | ||
|- | |- | ||
Line 2,634: | Line 2,970: | ||
|- | |- | ||
|[http://www.repeatmasker.org/RMBlast.html RMBlast] | |[http://www.repeatmasker.org/RMBlast.html RMBlast] | ||
− | |2.11.0-IGB-gcc-8.2.0<br>2.6.0-IGB-gcc-4.9.4<br>2.9.0-IGB-gcc-4.9.4 | + | |2.11.0-IGB-gcc-8.2.0<br>2.14.1-IGB-gcc-8.2.0<br>2.6.0-IGB-gcc-4.9.4<br>2.9.0-IGB-gcc-4.9.4 |
|RMBlast is a RepeatMasker compatible version of the standard NCBI blastn program. The primary difference between this distribution and the NCBI distribution is the addition of a new program "rmblastn" for use with RepeatMasker and RepeatModeler. | |RMBlast is a RepeatMasker compatible version of the standard NCBI blastn program. The primary difference between this distribution and the NCBI distribution is the addition of a new program "rmblastn" for use with RepeatMasker and RepeatModeler. | ||
|- | |- | ||
Line 2,646: | Line 2,982: | ||
|- | |- | ||
|[http://www.cbs.dtu.dk/cgi-bin/sw_request?rnammer RNAmmer] | |[http://www.cbs.dtu.dk/cgi-bin/sw_request?rnammer RNAmmer] | ||
− | |1.2-IGB-gcc-4.9.4-Perl-5.24.1 | + | |1.2-IGB-gcc-4.9.4-Perl-5.24.1<br>1.2-IGB-gcc-8.2.0-Perl-5.28.1 |
|Ribosomal RNA sub units | |Ribosomal RNA sub units | ||
|- | |- | ||
Line 2,664: | Line 3,000: | ||
|3.13.0-IGB-gcc-4.9.4-Perl-5.24.1 | |3.13.0-IGB-gcc-4.9.4-Perl-5.24.1 | ||
|Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (produced by Prokka (Seemann, 2014)) and calculates the pan genome. | |Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (produced by Prokka (Seemann, 2014)) and calculates the pan genome. | ||
+ | |- | ||
+ | |[https://www.ripp.rodeo/index.html rodeo2] | ||
+ | |2.4.2-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |RODEO evaluates one or many genes, characterizing a gene neighborhood based on the presence of profile hidden Markov models (pHMMs). | ||
|- | |- | ||
|[https://root.cern.ch/ root] | |[https://root.cern.ch/ root] | ||
|6.14.06-IGB-gcc-4.9.4<br>6.14.06-IGB-gcc-4.9.4-Python-2.7.13 | |6.14.06-IGB-gcc-4.9.4<br>6.14.06-IGB-gcc-4.9.4-Python-2.7.13 | ||
|A modular scientific software toolkit. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R. | |A modular scientific software toolkit. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R. | ||
+ | |- | ||
+ | |[https://github.com/Abe404/root_painter root-painter-trainer] | ||
+ | |0.2.19.1-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |RootPainter is a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter uses a client-server architecture, allowing users with a typical laptop to utilise a GPU on a more computationally powerful server. | ||
|- | |- | ||
|[http://younglab.wi.mit.edu/super_enhancer_code.html ROSE] | |[http://younglab.wi.mit.edu/super_enhancer_code.html ROSE] | ||
Line 2,685: | Line 3,029: | ||
|rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions. | |rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions. | ||
|- | |- | ||
− | |[ | + | |[https://github.com/Alexamk/RREFinder RREFinder] |
− | | | + | |1.0.2-IGB-gcc-8.2.0-Python-3.7.2 |
+ | |Bioinformatic application for the detection of RREs in protein sequences of interest | ||
+ | |- | ||
+ | |[https://github.com/rsa-tools/rsat-code RSAT] | ||
+ | |20230828-IGB-gcc-8.2.0 | ||
|We offer tools to analyse cis-regulatory elements in genome sequences, motif discovery (support genome-wide data sets like ChIP-seq), transcription factor binding motif analysis (quality assessment, comparisons and clustering), comparative genomics, analysis of regulatory variations | |We offer tools to analyse cis-regulatory elements in genome sequences, motif discovery (support genome-wide data sets like ChIP-seq), transcription factor binding motif analysis (quality assessment, comparisons and clustering), comparative genomics, analysis of regulatory variations | ||
|- | |- | ||
|[http://deweylab.github.io/RSEM/ RSEM] | |[http://deweylab.github.io/RSEM/ RSEM] | ||
− | |1.3.0-IGB-gcc-4.9.4<br>1.3.1-IGB-gcc-4.9.4 | + | |1.3.0-IGB-gcc-4.9.4<br>1.3.1-IGB-gcc-4.9.4<br>1.3.3-IGB-gcc-8.2.0 |
|RNA-Seq by Expectation-Maximization | |RNA-Seq by Expectation-Maximization | ||
|- | |- | ||
Line 2,696: | Line 3,044: | ||
|2.6.4-IGB-gcc-4.9.4-Python-2.7.13 | |2.6.4-IGB-gcc-4.9.4-Python-2.7.13 | ||
|RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. Some basic modules quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, transcript level RNA integrity etc. | |RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. Some basic modules quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, transcript level RNA integrity etc. | ||
+ | |- | ||
+ | |[https://posit.co/download/rstudio-desktop/ RStudio] | ||
+ | |2023.09.1-494 | ||
+ | |Used by millions of people weekly, the RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R and Python. | ||
|- | |- | ||
|[https://www.realtimegenomics.com/products/rtg-tools rtg-tools] | |[https://www.realtimegenomics.com/products/rtg-tools rtg-tools] | ||
Line 2,706: | Line 3,058: | ||
|- | |- | ||
|[https://www.rust-lang.org Rust] | |[https://www.rust-lang.org Rust] | ||
− | |1.41.1 | + | |1.41.1 |
|Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. | |Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. | ||
|- | |- | ||
Line 2,712: | Line 3,064: | ||
|20171114-IGB-gcc-4.9.4 | |20171114-IGB-gcc-4.9.4 | ||
|Next-generation sequencing can currently produce hundreds of millions of reads per lane of sample and that number increases at a dizzying rate. Barcoding individual sequences for multiple lines or multiple species is a cost-efficient method to sequence and analyze a broad range of data. | |Next-generation sequencing can currently produce hundreds of millions of reads per lane of sample and that number increases at a dizzying rate. Barcoding individual sequences for multiple lines or multiple species is a cost-efficient method to sequence and analyze a broad range of data. | ||
+ | |- | ||
+ | |[https://github.com/XDynames/SAI-app SAI-app] | ||
+ | |20230425-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |StomaAI application | ||
|- | |- | ||
|[https://github.com/COMBINE-lab/salmon Salmon] | |[https://github.com/COMBINE-lab/salmon Salmon] | ||
− | |0.11.3-IGB-gcc-4.9.4<br>0.12.0-IGB-gcc-8.2.0<br>0.13.1-IGB-gcc-8.2.0<br>0.14.1-IGB-gcc-8.2.0<br>0.8.2-IGB-gcc-4.9.4-Python-2.7.13<br>0.9.1-IGB-gcc-4.9.4<br>1.0.0-IGB-gcc-8.2.0<br>1.1.0-IGB-gcc-8.2.0<br>1.2.1-IGB-gcc-8.2.0<br>1.4.0-IGB-gcc-8.2.0<br>1.5.2-IGB-gcc-8.2.0 | + | |0.11.3-IGB-gcc-4.9.4<br>0.12.0-IGB-gcc-8.2.0<br>0.13.1-IGB-gcc-8.2.0<br>0.14.1-IGB-gcc-8.2.0<br>0.8.2-IGB-gcc-4.9.4-Python-2.7.13<br>0.9.1-IGB-gcc-4.9.4<br>1.0.0-IGB-gcc-8.2.0<br>1.1.0-IGB-gcc-8.2.0<br>1.10.0-IGB-gcc-8.2.0<br>1.2.1-IGB-gcc-8.2.0<br>1.4.0-IGB-gcc-8.2.0<br>1.5.2-IGB-gcc-8.2.0 |
|Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. | |Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. | ||
|- | |- | ||
Line 2,726: | Line 3,082: | ||
|- | |- | ||
|[https://github.com/marbl/SALSA SALSA] | |[https://github.com/marbl/SALSA SALSA] | ||
− | |2.2-IGB-gcc-4.9.4-Python-2.7.13<br> | + | |2.2-IGB-gcc-4.9.4-Python-2.7.13<br>2.3-IGB-gcc-4.9.4-Python-2.7.13 |
|A tool to scaffold long read assemblies with Hi-C | |A tool to scaffold long read assemblies with Hi-C | ||
+ | |- | ||
+ | |[https://github.com/weiquan/salt salt] | ||
+ | |beta0.2-IGB-gcc-8.2.0 | ||
+ | |alignment algorithm that is SNP-aware | ||
|- | |- | ||
|[http://lomereiter.github.io/sambamba/ sambamba] | |[http://lomereiter.github.io/sambamba/ sambamba] | ||
Line 2,738: | Line 3,098: | ||
|- | |- | ||
|[http://www.htslib.org/ SAMtools] | |[http://www.htslib.org/ SAMtools] | ||
− | |0.1.18-IGB-gcc-4.9.4<br>0.1.20-IGB-gcc-4.9.4<br>1.10-IGB-gcc-8.2.0<br>1.11-IGB-gcc-8.2.0<br>1.12-IGB-gcc-8.2.0<br>1.3.1-IGB-gcc-4.9.4<br>1.4-IGB-gcc-4.9.4<br>1.4.1-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.7-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4 | + | |0.1.18-IGB-gcc-4.9.4<br>0.1.20-IGB-gcc-4.9.4<br>0.1.20-IGB-gcc-8.2.0<br>1.10-IGB-gcc-8.2.0<br>1.11-IGB-gcc-8.2.0<br>1.12-IGB-gcc-8.2.0<br>1.17-IGB-gcc-8.2.0<br>1.3.1-IGB-gcc-4.9.4<br>1.4-IGB-gcc-4.9.4<br>1.4.1-IGB-gcc-4.9.4<br>1.5-IGB-gcc-4.9.4<br>1.7-IGB-gcc-4.9.4<br>1.9-IGB-gcc-4.9.4 |
|SAM Tools provide various utilities for manipulating alignments in the SAM format, including sorting, merging, indexing and generating alignments in a per-position format. | |SAM Tools provide various utilities for manipulating alignments in the SAM format, including sorting, merging, indexing and generating alignments in a per-position format. | ||
|- | |- | ||
Line 2,782: | Line 3,142: | ||
|- | |- | ||
|[https://www.sentieon.com/ sentieon] | |[https://www.sentieon.com/ sentieon] | ||
− | |201808<br>201911<br>202010.01<br>202112 | + | |201808<br>201911<br>202010.01<br>202112<br>202112.01<br>202112.04<br>202112.06<br>202308<br>202308.02 |
|A fast and accurate solution to variant calling from next-generation sequence data | |A fast and accurate solution to variant calling from next-generation sequence data | ||
|- | |- | ||
|[https://github.com/smirarab/sepp SEPP] | |[https://github.com/smirarab/sepp SEPP] | ||
− | |20180223-IGB-gcc-4.9.4-Python-2.7.13<br>4.3.10-IGB-gcc-8.2.0-Python-3.7.2 | + | |20180223-IGB-gcc-4.9.4-Python-2.7.13<br>4.3.10-IGB-gcc-8.2.0-Python-3.7.2<br>4.5.1-IGB-gcc-8.2.0-Python-3.7.2 |
|SEPP stands for "SATe-enabled Phylogenetic Placement", and addresses the problem of phylogenetic placement of short reads into reference alignments and trees. | |SEPP stands for "SATe-enabled Phylogenetic Placement", and addresses the problem of phylogenetic placement of short reads into reference alignments and trees. | ||
|- | |- | ||
|[https://www.seqan.de/ seqan] | |[https://www.seqan.de/ seqan] | ||
− | |2.3.2-IGB-gcc-4.9.4 | + | |2.2.0-IGB-gcc-8.2.0<br>2.3.2-IGB-gcc-4.9.4 |
|SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data | |SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data | ||
|- | |- | ||
|[https://bioinf.shenwei.me/seqkit seqkit] | |[https://bioinf.shenwei.me/seqkit seqkit] | ||
− | |0.12.1<br>2.0.0 | + | |0.12.1<br>0.15.0<br>2.0.0<br>2.3.0<br>2.5.1<br>2.6.1 |
|a cross-platform and ultrafast toolkit for FASTA/Q file manipulation | |a cross-platform and ultrafast toolkit for FASTA/Q file manipulation | ||
|- | |- | ||
Line 2,832: | Line 3,192: | ||
|3.0.7-IGB-gcc-4.9.4 | |3.0.7-IGB-gcc-4.9.4 | ||
|Sibelia is a tool for finding synteny blocks in closely related genomes, like different strains of the same bacterial species. | |Sibelia is a tool for finding synteny blocks in closely related genomes, like different strains of the same bacterial species. | ||
+ | |- | ||
+ | |[https://github.com/najoshi/sickle/ sickle] | ||
+ | |1.33-IGB-gcc-8.2.0 | ||
+ | |A windowed adaptive trimming tool for FASTQ files using quality | ||
|- | |- | ||
|[http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp SignalP] | |[http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp SignalP] | ||
|4.1 | |4.1 | ||
|Signal peptide and cleavage sites in gram+, gram- and eukaryotic amino acid sequences | |Signal peptide and cleavage sites in gram+, gram- and eukaryotic amino acid sequences | ||
+ | |- | ||
+ | |[https://github.com/sgoldenlab/simba simba] | ||
+ | |1.3.0 | ||
+ | |The SimBA region of interest (ROI) interface allows users to define and draw ROIs on videos. ROI data can be used to calculate basic descriptive statistics based on animals movements and locations such as: | ||
|- | |- | ||
|singularity | |singularity | ||
Line 2,844: | Line 3,212: | ||
|1.1-IGB-gcc-4.9.4-Perl-5.24.1 | |1.1-IGB-gcc-4.9.4-Perl-5.24.1 | ||
|Skylign is a tool for creating logos representing both sequence alignments and profile hidden Markov models. Submit to the form on the right in order to produce (i) interactive logos for inclusion in webpages, or (ii) static logos for use in documents. | |Skylign is a tool for creating logos representing both sequence alignments and profile hidden Markov models. Submit to the form on the right in order to produce (i) interactive logos for inclusion in webpages, or (ii) static logos for use in documents. | ||
+ | |- | ||
+ | |[https://sleap.ai/ sleap] | ||
+ | |1.2.4-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |SLEAP is an open source deep-learning based framework for multi-animal pose tracking. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading. | ||
|- | |- | ||
|[https://github.com/songjiajia2018/SMART-Aptamer-v1 SMART-Aptamer-v1] | |[https://github.com/songjiajia2018/SMART-Aptamer-v1 SMART-Aptamer-v1] | ||
Line 2,858: | Line 3,230: | ||
|- | |- | ||
|[https://www.pacb.com/support/software-downloads/ smrtlink] | |[https://www.pacb.com/support/software-downloads/ smrtlink] | ||
− | |10.0.0.108728<br>8.0.0.80529<br>9.0.0.92188 | + | |10.0.0.108728<br>11.0.0.146107<br>11.1.0.166339<br>8.0.0.80529<br>9.0.0.92188 |
|PacBio’s open-source SMRT Analysis software suite is designed for use with Single Molecule, Real-Time (SMRT) Sequencing data. You can analyze, visualize, and manage your data through an intuitive GUI or command-line interface. | |PacBio’s open-source SMRT Analysis software suite is designed for use with Single Molecule, Real-Time (SMRT) Sequencing data. You can analyze, visualize, and manage your data through an intuitive GUI or command-line interface. | ||
+ | |- | ||
+ | |[https://github.com/KamilSJaron/smudgeplot smudgeplot] | ||
+ | |0.2.5-IGB-gcc-8.2.0-Python-3.7.2 | ||
+ | |This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. | ||
|- | |- | ||
|[https://snakemake.readthedocs.io/en/stable/ snakemake] | |[https://snakemake.readthedocs.io/en/stable/ snakemake] | ||
Line 2,882: | Line 3,258: | ||
|- | |- | ||
|[http://sourceforge.net/projects/snpeff/ snpEff] | |[http://sourceforge.net/projects/snpeff/ snpEff] | ||
− | |4.3o-Java-1.8.0_121<br>4.3t-Java-1.8.0_152<br>5.0e-Java-15.0.1 | + | |4.3o-Java-1.8.0_121<br>4.3t-Java-1.8.0_152<br>5.0e-Java-15.0.1<br>5.1f-Java-15.0.1 |
|SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes). | |SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes). | ||
|- | |- | ||
Line 2,888: | Line 3,264: | ||
|1.0-IGB-gcc-4.9.4 | |1.0-IGB-gcc-4.9.4 | ||
|SNP-o-matic is a fast, stringent short-read mapping software. It supports a multitude of output types and formats, for uses in filtering reads, alignments, sequence-based genotyping calls, assisted reassembly of contigs etc. | |SNP-o-matic is a fast, stringent short-read mapping software. It supports a multitude of output types and formats, for uses in filtering reads, alignments, sequence-based genotyping calls, assisted reassembly of contigs etc. | ||
+ | |- | ||
+ | |[https://felixkrueger.github.io/SNPsplit/ SNPsplit] | ||
+ | |0.6.0-IGB-gcc-8.2.0-Perl-5.28.1 | ||
+ | |SNPsplit is an allele-specific alignment sorter which is designed to read alignment files in SAM/BAM format and determine the allelic origin of reads that cover known SNP positions. | ||
|- | |- | ||
|[http://snver.sourceforge.net/ SNVer] | |[http://snver.sourceforge.net/ SNVer] | ||
Line 2,906: | Line 3,286: | ||
|- | |- | ||
|[https://bioinfo.lifl.fr/RNA/sortmerna/ sortmerna] | |[https://bioinfo.lifl.fr/RNA/sortmerna/ sortmerna] | ||
− | |2.1 | + | |2.1<br>4.3.6 |
|SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. | |SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. | ||
|- | |- | ||
Line 2,912: | Line 3,292: | ||
|2.0.1-IGB-gcc-4.9.4-Python-3.6.1 | |2.0.1-IGB-gcc-4.9.4-Python-3.6.1 | ||
|Bayesian community-wide culture-independent microbial source tracking | |Bayesian community-wide culture-independent microbial source tracking | ||
+ | |- | ||
+ | |[https://github.com/sourmash-bio/sourmash sourmash] | ||
+ | |4.6.1-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |Quickly search, compare, and analyze genomic and metagenomic data sets. | ||
|- | |- | ||
|[http://http://sox.sourceforge.net/ SoX] | |[http://http://sox.sourceforge.net/ SoX] | ||
Line 2,918: | Line 3,302: | ||
|- | |- | ||
|[https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger spaceranger] | |[https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger spaceranger] | ||
− | |1.0.0<br>1.1.0<br>1.2.2<br>1.3.0 | + | |1.0.0<br>1.1.0<br>1.2.2<br>1.3.0<br>2.0.0<br>2.1.0 |
|Space Ranger is a set of analysis pipelines that process Visium spatial RNA-seq output and brightfield and fluorescence microscope images in order to detect tissue, align reads, generate feature-spot matrices, perform clustering and gene expression analysis, and place spots in spatial context on the slide image. | |Space Ranger is a set of analysis pipelines that process Visium spatial RNA-seq output and brightfield and fluorescence microscope images in order to detect tissue, align reads, generate feature-spot matrices, perform clustering and gene expression analysis, and place spots in spatial context on the slide image. | ||
|- | |- | ||
Line 2,926: | Line 3,310: | ||
|- | |- | ||
|[http://cab.spbu.ru/software/spades/ SPAdes] | |[http://cab.spbu.ru/software/spades/ SPAdes] | ||
− | |3.10.1-IGB-gcc-4.9.4-Python-2.7.13<br>3.11.0-IGB-gcc-4.9.4-Python-2.7.13<br>3.11.0-IGB-gcc-4.9.4-Python-3.6.1<br>3.11.1-IGB-gcc-4.9.4-Python-3.6.1<br>3.13.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.14.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.15.0-IGB-gcc-8.2.0-Python-3.7.2<br>3.15.3-IGB-gcc-8.2.0-Python-3.7.2 | + | |3.10.1-IGB-gcc-4.9.4-Python-2.7.13<br>3.11.0-IGB-gcc-4.9.4-Python-2.7.13<br>3.11.0-IGB-gcc-4.9.4-Python-3.6.1<br>3.11.1-IGB-gcc-4.9.4-Python-3.6.1<br>3.13.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.14.1-IGB-gcc-8.2.0-Python-3.7.2<br>3.15.0-IGB-gcc-8.2.0-Python-3.7.2<br>3.15.3-IGB-gcc-8.2.0-Python-3.7.2<br>3.15.5-IGB-gcc-8.2.0-Python-3.7.2 |
|SPAdes . St. Petersburg genome assembler . is intended for both standard isolates and single-cell MDA bacteria assemblies. | |SPAdes . St. Petersburg genome assembler . is intended for both standard isolates and single-cell MDA bacteria assemblies. | ||
|- | |- | ||
Line 2,946: | Line 3,330: | ||
|- | |- | ||
|[http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=std SRA-Toolkit] | |[http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=std SRA-Toolkit] | ||
− | |2.10.5<br>2.10.9<br>2.8.2-1 | + | |2.10.5<br>2.10.9<br>2.8.2-1<br>3.0.0 |
|The NCBI SRA Toolkit enables reading (dumping) of sequencing files from the SRA database and writing (loading) files into the .sra format | |The NCBI SRA Toolkit enables reading (dumping) of sequencing files from the SRA database and writing (loading) files into the .sra format | ||
|- | |- | ||
Line 2,962: | Line 3,346: | ||
|- | |- | ||
|[http://creskolab.uoregon.edu/stacks/ Stacks] | |[http://creskolab.uoregon.edu/stacks/ Stacks] | ||
− | |1.47-IGB-gcc-4.9.4<br>2.54-IGB-gcc-8.2.0 | + | |1.47-IGB-gcc-4.9.4<br>2.54-IGB-gcc-8.2.0<br>2.62-IGB-gcc-8.2.0 |
|Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography. | |Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography. | ||
|- | |- | ||
|[https://github.com/alexdobin/STAR STAR] | |[https://github.com/alexdobin/STAR STAR] | ||
− | |2.5.3a-IGB-gcc-4.9.4<br>2.6.0c-IGB-gcc-4.9.4<br>2.6.1b-IGB-gcc-4.9.4<br>2.7.0d-IGB-gcc-8.2.0<br>2.7.0f-IGB-gcc-8.2.0<br>2.7.3a-IGB-gcc-8.2.0<br>2.7.4a-IGB-gcc-8.2.0<br>2.7.6a-IGB-gcc-8.2.0 | + | |2.5.3a-IGB-gcc-4.9.4<br>2.6.0c-IGB-gcc-4.9.4<br>2.6.1b-IGB-gcc-4.9.4<br>2.7.0d-IGB-gcc-8.2.0<br>2.7.0f-IGB-gcc-8.2.0<br>2.7.10a-IGB-gcc-8.2.0<br>2.7.3a-IGB-gcc-8.2.0<br>2.7.4a-IGB-gcc-8.2.0<br>2.7.6a-IGB-gcc-8.2.0 |
|STAR aligns RNA-seq reads to a reference genome using uncompressed suffix arrays. | |STAR aligns RNA-seq reads to a reference genome using uncompressed suffix arrays. | ||
|- | |- | ||
Line 2,994: | Line 3,378: | ||
|- | |- | ||
|[http://subread.sourceforge.net/ Subread] | |[http://subread.sourceforge.net/ Subread] | ||
− | |1.5.2-IGB-gcc-4.9.4<br>1.6.3-IGB-gcc-4.9.4<br>2.0.0-IGB-gcc-8.2.0 | + | |1.5.2-IGB-gcc-4.9.4<br>1.6.3-IGB-gcc-4.9.4<br>2.0.0-IGB-gcc-8.2.0<br>2.0.4-IGB-gcc-8.2.0 |
|High performance read alignment, quantification and mutation discovery | |High performance read alignment, quantification and mutation discovery | ||
|- | |- | ||
Line 3,008: | Line 3,392: | ||
|1.1.5<br>1.2.0<br>1.2.1<br>2.0.0<br>2.0.1<br>2.1.0<br>2.1.1 | |1.1.5<br>1.2.0<br>1.2.1<br>2.0.0<br>2.0.1<br>2.1.0<br>2.1.1 | ||
|Supernova is a software package for de novo assembly from Chromium Linked-Reads that are made from a single whole-genome library from an individual DNA source. A key feature of Supernova is that it creates diploid assemblies, thus separately representing maternal and paternal chromosomes over very long distances. | |Supernova is a software package for de novo assembly from Chromium Linked-Reads that are made from a single whole-genome library from an individual DNA source. A key feature of Supernova is that it creates diploid assemblies, thus separately representing maternal and paternal chromosomes over very long distances. | ||
+ | |- | ||
+ | |[https://github.com/fritzsedlazeck/SURVIVOR SURVIVOR] | ||
+ | |1.0.7-IGB-gcc-8.2.0 | ||
+ | |SURVIVOR is a tool set for simulating/evaluating SVs, merging and comparing SVs within and among samples, and includes various methods to reformat or summarize SVs. | ||
|- | |- | ||
|[https://github.com/hall-lab/svtools svtools] | |[https://github.com/hall-lab/svtools svtools] | ||
Line 3,048: | Line 3,436: | ||
|1.1-IGB-gcc-4.9.4-Python-3.6.1 | |1.1-IGB-gcc-4.9.4-Python-3.6.1 | ||
|TagDigger is a program for processing FASTQ files from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) experiments. | |TagDigger is a program for processing FASTQ files from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) experiments. | ||
+ | |- | ||
+ | |[https://github.com/hyeshik/tailseeker tailseeker] | ||
+ | |3.2.1 | ||
+ | |Tailseeker is the official pipeline for TAIL-seq, which measures poly(A) tail lengths and 3′-end modifications with Illumina SBS sequencers. | ||
|- | |- | ||
|[https://github.com/songlab-cal/tape TAPE] | |[https://github.com/songlab-cal/tape TAPE] | ||
Line 3,074: | Line 3,466: | ||
|- | |- | ||
|[https://github.com/lanpa/tensorboardX tensorboardX] | |[https://github.com/lanpa/tensorboardX tensorboardX] | ||
− | |1.9-IGB-gcc-4.9.4-Python-3.6.1<br>2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.1-IGB-gcc-8.2.0-Python-3.7.2 | + | |1.9-IGB-gcc-4.9.4-Python-3.6.1<br>2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.5.1-IGB-gcc-8.2.0 |
− | | | + | |Tensorboard for PyTorch. |
|- | |- | ||
|[https://www.tensorflow.org/ Tensorflow] | |[https://www.tensorflow.org/ Tensorflow] | ||
− | |1.15.2-IGB-gcc-4.9.4-Python-3.6.1<br>1.2.1-IGB-gcc-4.9.4-Python-2.7.13<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1 | + | |1.15.2-IGB-gcc-4.9.4-Python-3.6.1<br>1.2.1-IGB-gcc-4.9.4-Python-2.7.13<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.8.2-IGB-gcc-8.2.0-Python-3.7.2<br>2.9.1-IGB-gcc-8.2.0-Python-3.7.2 |
|An open-source software library for Machine Intelligence | |An open-source software library for Machine Intelligence | ||
|- | |- | ||
|[https://www.tensorflow.org/ Tensorflow-GPU] | |[https://www.tensorflow.org/ Tensorflow-GPU] | ||
− | |1.13.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.14.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.2.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.5.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.9.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.0.3-IGB-gcc-4.9.4-Python-3.6.1<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-8.2.0-Python-3.7.2 | + | |1.13.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.14.0-IGB-gcc-4.9.4-Python-3.6.1<br>1.2.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.5.1-IGB-gcc-4.9.4-Python-3.6.1<br>1.9.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.0.3-IGB-gcc-4.9.4-Python-3.6.1<br>2.11.0-IGB-gcc-8.2.0-Python-3.7.2<br>2.2.0-IGB-gcc-4.9.4-Python-3.6.1<br>2.3.1-IGB-gcc-8.2.0-Python-3.7.2<br>2.5.3-IGB-gcc-8.2.0-Python-3.7.2<br>2.6.5-IGB-gcc-8.2.0-Python-3.7.2<br>2.9.1-IGB-gcc-8.2.0-Python-3.7.2 |
|An open-source software library for Machine Intelligence | |An open-source software library for Machine Intelligence | ||
|- | |- | ||
Line 3,130: | Line 3,522: | ||
|- | |- | ||
|[https://github.com/TransDecoder/TransDecoder/wiki TransDecoder] | |[https://github.com/TransDecoder/TransDecoder/wiki TransDecoder] | ||
− | |5.1.0-IGB-gcc-4.9.4-Perl-5.24.1<br>5.5.0-IGB-gcc-4.9.4-Perl-5.24.1 | + | |5.1.0-IGB-gcc-4.9.4-Perl-5.24.1<br>5.5.0-IGB-gcc-4.9.4-Perl-5.24.1<br>5.7.0-IGB-gcc-8.2.0-Perl-5.28.1 |
|TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks. | |TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks. | ||
+ | |- | ||
+ | |[https://github.com/huggingface/transformers transformers] | ||
+ | |4.40.2-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. | ||
|- | |- | ||
|[http://hibberdlab.com/transrate/ transrate] | |[http://hibberdlab.com/transrate/ transrate] | ||
Line 3,158: | Line 3,554: | ||
|- | |- | ||
|[http://trinityrnaseq.github.io/ Trinity] | |[http://trinityrnaseq.github.io/ Trinity] | ||
− | |2.10.0-IGB-gcc-8.2.0<br>2.4.0-IGB-gcc-4.9.4<br>2.5.0-IGB-gcc-4.9.4<br>2.6.5-IGB-gcc-4.9.4<br>2.8.5-IGB-gcc-4.9.4 | + | |2.10.0-IGB-gcc-8.2.0<br>2.14.0-IGB-gcc-8.2.0<br>2.15.1-IGB-gcc-8.2.0<br>2.4.0-IGB-gcc-4.9.4<br>2.5.0-IGB-gcc-4.9.4<br>2.6.5-IGB-gcc-4.9.4<br>2.8.5-IGB-gcc-4.9.4 |
|Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. | |Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. | ||
|- | |- | ||
|[https://trinotate.github.io/ Trinotate] | |[https://trinotate.github.io/ Trinotate] | ||
− | |3.1.1-IGB-gcc-4.9.4-Perl-5.24.1<br>3.2.1-IGB-gcc-4.9.4-Perl-5.24.1 | + | |3.1.1-IGB-gcc-4.9.4-Perl-5.24.1<br>3.2.1-IGB-gcc-4.9.4-Perl-5.24.1<br>4.0.0-IGB-gcc-8.2.0-Perl-5.28.1 |
|Trinotate is a comprehensive annotation suite designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms. Trinotate makes use of a number of different well referenced methods for functional annotation including homology search to known sequence data (BLAST+/SwissProt), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (signalP/tmHMM), and leveraging various annotation databases (eggNOG/GO/Kegg databases). All functional annotation data derived from the analysis of transcripts is integrated into a SQLite database which allows fast efficient searching for terms with specific qualities related to a desired scientific hypothesis or a means to create a whole annotation report for a transcriptome. | |Trinotate is a comprehensive annotation suite designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms. Trinotate makes use of a number of different well referenced methods for functional annotation including homology search to known sequence data (BLAST+/SwissProt), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (signalP/tmHMM), and leveraging various annotation databases (eggNOG/GO/Kegg databases). All functional annotation data derived from the analysis of transcripts is integrated into a SQLite database which allows fast efficient searching for terms with specific qualities related to a desired scientific hypothesis or a means to create a whole annotation report for a transcriptome. | ||
|- | |- | ||
Line 3,176: | Line 3,572: | ||
|0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | |0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | ||
|Trycycler is a tool for generating consensus long-read assemblies for bacterial genomes. I.e. if you have multiple long-read assemblies for the same isolate, Trycycler can combine them into a single assembly that is better than any of your inputs. | |Trycycler is a tool for generating consensus long-read assemblies for bacterial genomes. I.e. if you have multiple long-read assemblies for the same isolate, Trycycler can combine them into a single assembly that is better than any of your inputs. | ||
+ | |- | ||
+ | |[https://github.com/Gaius-Augustus/TSEBRA TSEBRA] | ||
+ | |1.0.3-IGB-gcc-8.2.0 | ||
+ | |TSEBRA is a combiner tool that selects transcripts from gene predictions based on the support by extrisic evidence in form of introns and start/stop codons. It was developed to combine BRAKER11 and BRAKER22 predicitons to increase their accuracies. | ||
|- | |- | ||
|[https://github.com/Generade-nl/TULIP TULIP] | |[https://github.com/Generade-nl/TULIP TULIP] | ||
Line 3,214: | Line 3,614: | ||
|- | |- | ||
|[http://www.drive5.com/usearch/index.html USEARCH] | |[http://www.drive5.com/usearch/index.html USEARCH] | ||
− | |11.0.667<br>6.1.544<br>7.0.1090<br>9.2.64 | + | |11.0.667<br>11.0.667-akent<br>6.1.544<br>7.0.1090<br>9.2.64 |
|USEARCH is a unique sequence analysis tool which offers search and clustering algorithms that are often orders of magnitude faster than BLAST. | |USEARCH is a unique sequence analysis tool which offers search and clustering algorithms that are often orders of magnitude faster than BLAST. | ||
|- | |- | ||
Line 3,232: | Line 3,632: | ||
|2.3.9-Java-1.8.0_152 | |2.3.9-Java-1.8.0_152 | ||
| | | | ||
+ | |- | ||
+ | |[https://github.com/10XGenomics/vartrix vartrix] | ||
+ | |1.1.22 | ||
+ | |VarTrix is a software tool for extracting single cell variant information from 10x Genomics single cell data. VarTrix will take a set of previously defined variant calls and use that to identify those variants in the single cell data. | ||
|- | |- | ||
|[http://www.cse.lehigh.edu/~chen/software.htm VASP-E] | |[http://www.cse.lehigh.edu/~chen/software.htm VASP-E] | ||
Line 3,258: | Line 3,662: | ||
|- | |- | ||
|[https://www.ebi.ac.uk/~zerbino/velvet/ velvet] | |[https://www.ebi.ac.uk/~zerbino/velvet/ velvet] | ||
− | |1.2.10-IGB-gcc-4.9.4-kmer_121 | + | |1.2.10-IGB-gcc-4.9.4-kmer_121<br>1.2.10-IGB-gcc-8.2.0-kmer_121 |
|Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom. | |Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom. | ||
|- | |- | ||
Line 3,296: | Line 3,700: | ||
|2.4.3-IGB-gcc-4.9.4 | |2.4.3-IGB-gcc-4.9.4 | ||
|VSEARCH stands for vectorized search, as the tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. - Homepage: https://github.com/torognes/vsearch | |VSEARCH stands for vectorized search, as the tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. - Homepage: https://github.com/torognes/vsearch | ||
+ | |- | ||
+ | |[https://weblogo.threeplusone.com/ weblogo] | ||
+ | |3.7.12-IGB-gcc-8.2.0-Python-3.10.1 | ||
+ | |WebLogo is a web-based application designed to make the generation of sequence logos easy and painless. WebLogo has been featured in over 10000 scientific publications. | ||
|- | |- | ||
|[https://github.com/whatshap/whatshap whatshap] | |[https://github.com/whatshap/whatshap whatshap] | ||
|1.0-IGB-gcc-8.2.0-Python-3.7.2 | |1.0-IGB-gcc-8.2.0-Python-3.7.2 | ||
|WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads. | |WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads. | ||
+ | |- | ||
+ | |[http://didgeridoo.une.edu.au/km/wombat.php wombat] | ||
+ | |20210107 | ||
+ | |WOMBAT is a program to facilitate analyses fitting a linear, mixed model via restricted maximum likelihood (REML). | ||
|- | |- | ||
|[https://github.com/ruanjue/wtdbg2 wtdbg2] | |[https://github.com/ruanjue/wtdbg2 wtdbg2] | ||
Line 3,306: | Line 3,718: | ||
|- | |- | ||
|[https://www.wxpython.org/ wxPython] | |[https://www.wxpython.org/ wxPython] | ||
− | |4.1.0-IGB-gcc-4.9.4-Python-3.6.1 | + | |4.1.0-IGB-gcc-4.9.4-Python-3.6.1<br>4.1.0-IGB-gcc-8.2.0-Python-3.7.2 |
|the cross-platform GUI toolkit for the Python language. With wxPython software developers can create truly native user interfaces for their Python applications, that run with little or no modifications on Windows, Macs and Linux or other unix-like systems. | |the cross-platform GUI toolkit for the Python language. With wxPython software developers can create truly native user interfaces for their Python applications, that run with little or no modifications on Windows, Macs and Linux or other unix-like systems. | ||
|- | |- | ||
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|2.1-IGB-gcc-8.2.0-Python-3.10.1 | |2.1-IGB-gcc-8.2.0-Python-3.10.1 | ||
|xPore is a Python package for identification of differentail RNA modifications from Nanopore sequencing data. | |xPore is a Python package for identification of differentail RNA modifications from Nanopore sequencing data. | ||
+ | |- | ||
+ | |[https://github.com/parklab/xTea xTea] | ||
+ | |0.1.6 | ||
+ | |xTea (comprehensive transposable element analyzer) is designed to identify TE insertions from paired-end Illumina reads, barcode linked-reads, long reads (PacBio or Nanopore), or hybrid data from different sequencing platforms and takes whole-exome sequencing (WES) or whole-genome sequencing (WGS) data as input. | ||
|- | |- | ||
|[http://tukaani.org/xz/ XZ] | |[http://tukaani.org/xz/ XZ] | ||
|5.2.3-IGB-gcc-4.9.4<br>5.2.3-IGB-gcc-8.2.0 | |5.2.3-IGB-gcc-4.9.4<br>5.2.3-IGB-gcc-8.2.0 | ||
|xz: XZ utilities | |xz: XZ utilities | ||
+ | |- | ||
+ | |[https://github.com/c-zhou/yahs yahs] | ||
+ | |1.2a.2-IGB-gcc-8.2.0 | ||
+ | |YaHS is a scaffolding tool using Hi-C data. It relies on a new algothrim for contig joining detection which considers the topological distribution of Hi-C signals aiming to distingush real interaction signals from mapping nosies. | ||
|- | |- | ||
|Yasm | |Yasm | ||
Line 3,333: | Line 3,753: | ||
|Yasm: Complete rewrite of the NASM assembler with BSD license | |Yasm: Complete rewrite of the NASM assembler with BSD license | ||
|- | |- | ||
− | | | + | |ZeroMQ |
− | | | + | | |
|ZeroMQ looks like an embeddable networking library but acts like a concurrency framework. It gives you sockets that carry atomic messages across various transports like in-process, inter-process, TCP, and multicast. You can connect sockets N-to-N with patterns like fanout, pub-sub, task distribution, and request-reply. It is fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built as asynchronous message-processing tasks. It has a score of language APIs and runs on most operating systems. | |ZeroMQ looks like an embeddable networking library but acts like a concurrency framework. It gives you sockets that carry atomic messages across various transports like in-process, inter-process, TCP, and multicast. You can connect sockets N-to-N with patterns like fanout, pub-sub, task distribution, and request-reply. It is fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built as asynchronous message-processing tasks. It has a score of language APIs and runs on most operating systems. | ||
|- | |- | ||
Line 3,346: | Line 3,766: | ||
|- | |- | ||
|[https://facebook.github.io/zstd zstd] | |[https://facebook.github.io/zstd zstd] | ||
− | |1.4.4-IGB-gcc-4.9.4 | + | |1.4.4-IGB-gcc-4.9.4<br>1.5.5-IGB-gcc-8.2.0 |
|Zstandard is a real-time compression algorithm, providing high compression ratios. It offers a very wide range of compression/speed trade-off, while being backed by a very fast decoder. It also offers a special mode for small data, called dictionary compression, and can create dictionaries from any sample set. | |Zstandard is a real-time compression algorithm, providing high compression ratios. It offers a very wide range of compression/speed trade-off, while being backed by a very fast decoder. It also offers a special mode for small data, called dictionary compression, and can create dictionaries from any sample set. | ||
|} | |} |
Revision as of 03:00, 14 May 2024
Application | Installed Versions | Description |
---|---|---|
3d-dna | 20190801-IGB-gcc-8.2.0-Python-3.7.2 | De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds |
454 | 2.8 | The GS Data Analysis Software package includes the tools to investigate complex genomic variation in samples including de novo assembly, reference guided alignment and variant calling, and low abundance variant identification and quantification. - Homepage: http://454.com/products/analysis-software/index.asp |
a2ps | 4.14-IGB-gcc-4.9.4 4.14-IGB-gcc-8.2.0 |
a2ps-4.14: Formats an ascii file for printing on a postscript printer |
abcranger | 1.2.64 | Random forests methodologies for ABC model choice and ABC Bayesian parameter inference ( |
Abseil | 20230125.2-IGB-gcc-8.2.0 20230125.3-IGB-gcc-8.2.0 |
Abseil is an open-source collection of C++ library code designed to augment theC++ standard library. The Abseil library code is collected from Google's ownC++ code base, has been extensively tested and used in production, and is thesame code we depend on in our daily coding lives. |
ABySS | 2.0.2-IGB-gcc-4.9.4 | ABySS is a de novo, parallel, paired-end sequence assembler that is designed for short reads. The single-processor version is useful for assembling genomes up to 100 Mbases in size. The parallel version is implemented using MPI and is capable of assembling larger genomes. |
abyss | 2.2.5-IGB-gcc-8.2.0 | ABySS is a de novo sequence assembler intended for short paired-end reads and large genomes. |
AdapterRemoval | 2.1.7-IGB-gcc-4.9.4 | This program searches for and removes remnant adapter sequences from High-Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3 end of reads following adapter removal. |
AdmixTools | 1.0.1-IGB-gcc-4.9.4 | The ADMIXTOOLS package implements 5 methods described in Patterson et al. (2012) Ancient Admixture in Human History. Details of the methods and algorithm can be found in this paper. |
admixture | 1.3.0 | ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm. |
ADOL-C | 2.6.3-IGB-gcc-4.9.4 | ADOL-C is an open-source package for the automatic differentiation of C and C++ programs. |
AGAT | 0.5.1-IGB-gcc-8.2.0-Perl-5.28.1 | Suite of tools to handle gene annotations in any GTF/GFF format. |
AGEnt | 0.2.1-IGB-gcc-4.9.4 | AGEnt performs in silico subtractive hybridization of core genome sequences, such as those produced by Spine, against a query genomic sequence to identify accessory genomic sequences (AGEs) in the query genome. Sequences are aligned using Nucmer, outputting sequences and sequence characteristics of those regions in the query genome that are not found in the core genome. If gene coordinate information is provided, a list of accessory genes in the query genome will also be output. |
albacore | 2.0.2-IGB-gcc-4.9.4-Python-3.6.1 2.1.10-IGB-gcc-4.9.4-Python-3.6.1 2.3.1-IGB-gcc-4.9.4-Python-3.6.1 |
Local basecalling for MinKNOW |
alevin-fry | 0.4.2 | alevin-fry is a suite of tools for the rapid, accurate and memory-frugal processing single-cell and single-nucleus sequencing data. |
alfa | 1.1.1-IGB-gcc-8.2.0-Python-3.7.2 | ALFA provides a global overview of features distribution composing NGS dataset(s). |
AlignGraph | 20180222-IGB-gcc-4.9.4 | AlignGraph is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism. |
alphafold | 2.1.2 2.3.1 2.3.2 |
This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. |
AMOS | 3.1.0-IGB-gcc-4.9.4 | The AMOS consortium is committed to the development of open-source whole genome assembly software. |
AMPHORA2 | 20190104-IGB-gcc-4.9.4 | An Automated Phylogenomic Inference Pipeline for Bacterial and Archaeal Sequences. |
Anacapa | 20200814-IGB-gcc-4.9.4-Python-2.7.13 | Anacapa is an eDNA toolkit that allows users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data. |
Anaconda2 | 4.3.1 | Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. - Homepage: https://www.continuum.io/anaconda-overview |
Anaconda3 | 2019.10 2022.05 2023.09 5.0.1 5.1.0 |
Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. |
ANGSD | 0.933-IGB-gcc-4.9.4 0.941-IGB-gcc-8.2.0 |
ANGSD is a software for analyzing next generation sequencing data. The software can handle a number of different input types from mapped reads to imputed genotype probabilities. |
ANIcalculator | 1.0 | |
ANNOVAR | 2019Oct24-IGB-gcc-8.2.0-Perl-5.28.1 | ANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, hg38, as well as mouse, worm, fly, yeast and many others). |
ant | 1.10.1-Java-1.8.0_121 1.10.1-Java-1.8.0_152 1.10.10-Java-15.0.1 1.10.13-Java-15.0.1 1.10.9-Java-1.8.0_201 |
Apache Ant is a Java library and command-line tool whose mission is to drive processes described in build files as targets and extension points dependent upon each other. The main known usage of Ant is the build of Java applications. |
antismash | 4.1.0 5.1.2 6.1.0 7.1.0-0 |
antiSMASH allows the rapid genome-wide identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genomes. It integrates and cross-links with a large number of in silico secondary metabolite analysis tools that have been published earlier. |
ANTLR | ANTLR, ANother Tool for Language Recognition, (formerly PCCTS) is a language tool that provides a framework for constructing recognizers, compilers, and translators from grammatical descriptions containing Java, C#, C++, or Python actions. | |
anvio | 5.5-IGB-gcc-4.9.4-Python-3.6.1 6.1-IGB-gcc-4.9.4-Python-3.6.1 7.1-IGB-gcc-4.9.4-Python-3.6.1 |
Anvi’o is an open-source, community-driven analysis and visualization platform for ‘omics data. |
any2fasta | 0.4.2-IGB-gcc-8.2.0-Perl-5.28.1 | Convert various sequence formats to FASTA |
apollo | 20200510-IGB-gcc-8.2.0 | A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm |
ARB | 6.0.6 | The ARB software is a graphically oriented package comprising various tools for sequence database handling and data analysis. A central database of processed (aligned) sequences and any type of additional data linked to the respective sequence entries is structured according to phylogeny or other user defined criteria |
ArcadeLearningEnvironment | 0.5.1-IGB-gcc-4.9.4-Python-2.7.13 | The Arcade Learning Environment (ALE) -- a platform for AI research. - Homepage: https://github.com/mgbellemare/Arcade-Learning-Environment |
ARCS | 1.0.0-IGB-gcc-4.9.4-Perl-5.24.1 1.2.1-IGB-gcc-8.2.0-Perl-5.28.1 |
Scaffolding genome sequence assemblies using 10X Genomics GemCode/Chromium data. |
argtable | Argtable is an ANSI C library for parsing GNU style command line options with a minimum of fuss. | |
aria2 | 1.36.0-IGB-gcc-8.2.0 1.37.0-IGB-gcc-8.2.0 |
aria2 is a lightweight multi-protocol & multi-source command-line download utility. |
Arlequin | 3.5 | An Integrated Software for Population Genetics Data Analysis |
ASEr | 0.2-IGB-gcc-8.2.0-Python-3.7.2 | Get ASE counts from BAMs or raw fastq data |
aspera | 3.7.6 4.2.7.445 |
Aspera’s unwavering mission is to create the next-generation software technologies that move the world’s data at maximum speed, regardless of file size, transfer distance and network conditions. |
Assemblytics | 3f570cd-IGB-gcc-4.9.4 df5361f-IGB-gcc-4.9.4 |
Analyze your assembly by comparing it to a reference genome |
asset | 1.0.3-IGB-gcc-8.2.0 | Assembly evaluation tool |
atactk | 0.1.6-IGB-gcc-4.9.4-Python-2.7.13 | a toolkit for ATAC-seq data |
ATK | ATK provides the set of accessibility interfaces that are implemented by other toolkits and applications. Using the ATK interfaces, accessibility tools have full access to view and control running applications. | |
AUGUSTUS | 3.3-IGB-gcc-4.9.4 | AUGUSTUS is a program that predicts genes in eukaryotic genomic sequences |
augustus | 3.2.3-IGB-gcc-4.9.4 3.3.2-IGB-gcc-4.9.4 3.3.3-IGB-gcc-8.2.0 3.4.0-IGB-gcc-8.2.0 |
AUGUSTUS is a program that predicts genes in eukaryotic genomic sequences |
Autoconf | Autoconf is an extensible package of M4 macros that produce shell scripts to automatically configure software source code packages. These scripts can adapt the packages to many kinds of UNIX-like systems without manual user intervention. Autoconf creates a configuration script for a package from a template file that lists the operating system features that the package can use, in the form of M4 macro calls. - Homepage: http://www.gnu.org/software/autoconf/ | |
Automake | Automake: GNU Standards-compliant Makefile generator | |
Autotools | This bundle collect the standard GNU build tools: Autoconf, Automake and libtool | |
awkde | 20220617-IGB-gcc-8.2.0-Python-3.7.2 | This uses the awesome pybind11 package which makes creating C++ bindings super convenient. Only the evaluation is written in a small C++ snippet to speed it up, the rest is a pure python implementation. |
awscli | 1.16.113-IGB-gcc-4.9.4-Python-3.6.1 1.18.96-IGB-gcc-8.2.0-Python-3.7.2 |
The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services. With just one tool to download and configure, you can control multiple AWS services from the command line and automate them through scripts. |
bam-readcount | 0.8.0-IGB-gcc-4.9.4 | The purpose of this program is to generate metrics at single nucleotide positions. |
BamM | 1.7.3-IGB-gcc-4.9.4-Python-2.7.13 | The primary motivation for building BamM was to replaace PySam in GroopM. Not saying PySam is bad, it's just that GroopM doesn't need all the PySam features and what it does need can be done way way faster in C-land. |
bamm-suite | 20191127-IGB-gcc-4.9.4-Python-3.6.1 | BaMM-suite is the motif finding suite developed by the Soedinglab. |
bammds | 20140602-IGB-gcc-4.9.4 | Software that allows to create a multidimensional scaling (MDS) plot of populations for genetic data. |
BaMMmotif | 2.0 | Bayesian Markov Model motif discovery software (version 2). |
BamTools | 2.4.1-IGB-gcc-4.9.4 2.5.1-IGB-gcc-4.9.4 2.5.1-IGB-gcc-8.2.0 |
BamTools provides both a programmers API and an end-users toolkit for handling BAM files. |
barrnap | 0.9-IGB-gcc-4.9.4 | Barrnap predicts the location of ribosomal RNA genes in genomes. |
bart2 | 20240302-IGB-gcc-8.2.0-Python-3.7.2 | BART (Binding Analysis for Regulation of Transcription) is a bioinformatics tool for predicting functional transcriptional regulators (TRs) that bind at genomic cis-regulatory regions to regulate gene expression in the human or mouse genomes, taking a query gene set, a ChIP-seq dataset or a scored genomic region set as input. |
basespace-cli | 1.5.1 | You can work with your BaseSpace Sequence Hub data using the command line interface (CLI). The BaseSpace Sequence Hub CLI supports scripting and programmatic access to BaseSpace Sequence Hub for automation, bulk operations, and other routine functions. It can be used independently or in conjunction with BaseMount. |
bax2bam | 20171114-IGB-gcc-4.9.4 | bax2bam converts the legacy PacBio basecall format (bax.h5) into the BAMbasecall format. |
BayeScan | 2.1 | This program, BayeScan aims at identifying candidate loci under natural selection from genetic data, using differences in allele frequencies between populations. BayeScan is based on the multinomial-Dirichlet model. |
bazel | 0.6.0-Java-1.8.0_121 | Bazel is a build tool that builds code quickly and reliably. It is used to build the majority of Googles software. |
BBMap | 38.36-Java-1.8.0_152 38.94-Java-1.8.0_201 |
BBMap short read aligner, and other bioinformatic tools. |
BCFtools | 1.12-IGB-gcc-8.2.0 1.17-IGB-gcc-8.2.0 1.4-IGB-gcc-4.9.4 1.5-IGB-gcc-4.9.4 1.7-IGB-gcc-4.9.4 1.9-IGB-gcc-4.9.4 |
BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF |
bcl | 4.0.0 | |
bcl2fastq2 | 2.20 2.20-IGB-gcc-8.2.0 |
The bcl2fastq2 Conversion Software v2.20.0 can be used to convert BCL files from MiniSeq, MiSeq, NextSeq, HiSeq, and NovaSeq sequening systems. For conversion of data generated on Illumina sequencing systems using versions of RTA earlier than RTA 1.18.54, use bcl2fastq v1.8.4. |
beagle | 03Jul18.40b-Java-1.8.0_152 5.1-Java-1.8.0_152 |
Beagle is a software package for phasing genotypes and for imputing ungenotyped markers. Version 5.0 has new, fast algorithms for genotype phasing and imputation. |
beagle-lib | 4.0.0-IGB-gcc-8.2.0 | BEAGLE is a high-performance library that can perform the core calculations at the heart of most Bayesian and Maximum Likelihood phylogenetics packages. It can make use of highly-parallel processors such as those in graphics cards (GPUs) found in many PCs. |
BEAST2 | 2.7.5-IGB-gcc-8.2.0 | BEAST 2 is a cross-platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies using strict or relaxed molecular clock models. |
beast2 | 2.6.7-Java-1.8.0_201 | BEAST 2 is a cross-platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies using strict or relaxed molecular clock models. |
bedops | 2.4.30 | BEDOPS is an open-source command-line toolkit that performs highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale. |
BEDTools | 2.21.0-IGB-gcc-4.9.4 2.26.0-IGB-gcc-4.9.4 2.28.0-IGB-gcc-8.2.0 |
The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. |
Bifrost | 1.0.4-IGB-gcc-8.2.0 | Parallel construction, indexing and querying of colored and compacted de Bruijn graphs |
big-map2-analyse | 20200124 | This is the Github repository for the Biosynthetic Gene cluster Meta’omics abundance Profiler |
big-map2-process | 20200124 | This is the Github repository for the Biosynthetic Gene cluster Meta’omics abundance Profiler |
BiG-SCAPE | 1.0.1-IGB-gcc-4.9.4-Python-3.6.1 1.1.5-IGB-gcc-8.2.0-Python-3.7.2 |
BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) is a software package, written in Python, that constructs sequence similarity networks of Biosynthetic Gene Clusters (BGCs) and groups them into Gene Cluster Families (GCFs) |
binutils | binutils: GNU binary utilities | |
bio-embeddings | 0.2.2-IGB-gcc-8.2.0-Python-3.7.2 | Quickly predict protein structure and function from sequence via embeddings: |
bio-rocker | 1.2.0-IGB-gcc-4.9.4-Ruby-2.4.2 | Accurately detecting functional genes in metagenomes. |
bioawk | 1.0-IGB-gcc-8.2.0 | Bioawk is an extension to Brian Kernighan's awk, adding the support of several common biological data formats, including optionally gzip'ed BED, GFF, SAM, VCF, FASTA/Q and TAB-delimited formats with column names. |
biodatabase | 1.0-IGB-gcc-4.9.4 | Scripts to create databases on the biodatabase machine. |
biom-format | 2.1.6-IGB-gcc-4.9.4-Python-2.7.13 2.1.6-IGB-gcc-4.9.4-Python-3.6.1 2.1.8-IGB-gcc-4.9.4-Python-3.6.1 2.1.8-IGB-gcc-8.2.0-Python-3.7.2 |
The BIOM file format (canonically pronounced biome) is designed to be a general-useformat for representing biological sample by observation contingency tables. |
BioPerl | 1.7.1-IGB-gcc-4.9.4-Perl-5.24.1 1.7.1-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 1.7.2-IGB-gcc-8.2.0-Perl-5.28.1 |
Bioperl is the product of a community effort to produce Perl code which is useful in biology. Examples include Sequence objects, Alignment objects and database searching objects. |
Biopieces | 2.0-IGB-gcc-4.9.4-Perl-5.24.1 | Biopieces is a bioinformatic framework of tools easily used and easily created. |
Biopython | 1.68-IGB-gcc-4.9.4-Python-2.7.13 1.68-IGB-gcc-4.9.4-Python-3.6.1 1.76-IGB-gcc-4.9.4-Python-3.6.1 1.76-IGB-gcc-8.2.0-Python-3.7.2 1.79-IGB-gcc-8.2.0-Python-3.7.2 1.83-IGB-gcc-8.2.0-Python-3.10.1 |
Biopython is a set of freely available tools for biological computation writtenin Python by an international team of developers. It is a distributed collaborative effort todevelop Python libraries and applications which address the needs of current and future work inbioinformatics. |
bioservices | 1.7.9-IGB-gcc-4.9.4-Python-3.6.1 | Bioservices is a Python package that provides access to many Bioinformatices Web Services (e.g., UniProt) and a framework to easily implement Web Services wrappers (based on WSDL/SOAP or REST protocols). |
Bismark | 0.17.0-IGB-gcc-4.9.4-Perl-5.24.1 0.18.1-IGB-gcc-4.9.4-Perl-5.24.1 0.22.1-IGB-gcc-4.9.4-Perl-5.24.1 0.22.3-IGB-gcc-8.2.0-Perl-5.28.1 |
A tool to map bisulfite converted sequence reads and determine cytosine methylation states |
Bison | Bison is a general-purpose parser generator that converts an annotated context-free grammarinto a deterministic LR or generalized LR (GLR) parser employing LALR(1) parser tables. | |
BLASR | 4.0.0-IGB-gcc-4.9.4 | BLASR (Basic Local Alignment with Successive Refinement) rapidly maps reads to genomes by finding the highest scoring local alignment or set of local alignments between the read and the genome. Optimized for PacBios extraordinarily long reads and taking advantage of rich quality values, BLASR maps reads rapidly with high accuracy. |
BLAST | 2.2.26-Linux_x86_64 | Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. |
BLAST+ | 2.10.1-IGB-gcc-8.2.0 2.13.0-IGB-gcc-8.2.0 2.2.31-IGB-gcc-4.9.4 2.6.0-IGB-gcc-4.9.4 2.7.1-IGB-gcc-4.9.4 2.9.0-IGB-gcc-4.9.4 |
Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. |
BLAT | 3.5-IGB-gcc-4.9.4 | BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 25 bases or more. |
blobtools | 0.9.19.6-IGB-gcc-4.9.4-Python-2.7.13 1.0.1-IGB-gcc-4.9.4-Python-2.7.13 1.1.1-IGB-gcc-4.9.4-Python-3.6.1 |
Application for the visualisation of (draft) genome assemblies using TAGC (Taxon-annotated Gc-Coverage) plots |
blobtools2 | 2.6.1-IGB-gcc-4.9.4-Python-3.6.1 2.6.4-IGB-gcc-8.2.0-Python-3.7.2 |
Application for the visualisation of (draft) genome assemblies using TAGC (Taxon-annotated Gc-Coverage) plots |
bnpm | v7.20.0-IGB-gcc-4.9.4 | the package manager for JavaScript |
Boost | Boost provides free peer-reviewed portable C++ source libraries. | |
Boost.Python | Boost provides free peer-reviewed portable C++ source libraries. | |
Bowtie | 1.1.2-IGB-gcc-4.9.4 1.2.0-IGB-gcc-4.9.4 1.2.2-IGB-gcc-4.9.4 1.3.0-IGB-gcc-8.2.0 |
Bowtie is an ultrafast, memory-efficient short read aligner.It aligns short DNA sequences (reads) to the human genome. |
Bowtie2 | 2.1.0-IGB-gcc-4.9.4 2.3.1-IGB-gcc-4.9.4 2.3.2-IGB-gcc-4.9.4 2.3.5.1-IGB-gcc-4.9.4 2.4.1-IGB-gcc-8.2.0 2.4.2-IGB-gcc-8.2.0 2.4.5-IGB-gcc-8.2.0 2.5.3-IGB-gcc-8.2.0 |
Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes. |
Bracken | 2.6.2-IGB-gcc-4.9.4 2.6.2-IGB-gcc-8.2.0 |
Bracken is a companion program to Kraken 1 or Kraken 2 While Kraken classifies reads to multiple levels in the taxonomic tree, Bracken allows estimation of abundance at a single level using those classifications (e.g. Bracken can estimate abundance of species within a sample). |
BRAKER | 2.1.2-IGB-gcc-4.9.4 2.1.5-IGB-gcc-4.9.4 2.1.5-IGB-gcc-8.2.0 2.1.6-IGB-gcc-8.2.0 3.0.3-IGB-gcc-8.2.0 |
BRAKER2 is an extension of BRAKER1 which allows for fully automated training of the gene prediction tools GeneMark-EX and AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction. |
BreakSeq2 | 2.2-IGB-gcc-4.9.4-Python-2.7.13 | Ultrafast and accurate nucleotide-resolution analysis of structural variants - Homepage: http://bioinform.github.io/breakseq2/ |
breseq | 0.31.0-IGB-gcc-4.9.4 0.36.1-IGB-gcc-8.2.0 0.37.0-IGB-gcc-8.2.0 |
is a computational pipeline for the analysis of short-read re-sequencing data (e.g. Illumina, 454, IonTorrent, etc.). It uses reference-based alignment approaches to predict mutations in a sample relative to an already sequenced genome. |
BRIG | 0.95-Java-1.8.0_152 | BRIG is a free cross-platform (Windows/Mac/Unix) application that can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data. |
BS-Seeker | 2.1.2-IGB-gcc-4.9.4-Python-2.7.13 | BS Seeker 2 is a seamless and versatile pipeline for accurately and fast mapping the bisulfite-treated short reads. |
BS-Snper | 20170222-IGB-gcc-4.9.4-Perl-5.24.1 | BS-SNPer is an ultrafast and memory-efficient package, a program for BS-Seq variation detection from alignments in standard BAM/SAM format using approximate Bayesian modeling. |
BUSCO | 3.0.1-IGB-gcc-4.9.4-Python-2.7.13 4.1.4-IGB-gcc-8.2.0-Python-3.7.2 5.1.2-IGB-gcc-8.2.0-Python-3.7.2 5.3.2-IGB-gcc-8.2.0-Python-3.7.2 5.4.4-IGB-gcc-8.2.0-Python-3.7.2 5.5.0-IGB-gcc-8.2.0-Python-3.7.2 |
Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (BUSCO) |
BWA | 0.5.9-IGB-gcc-4.9.4 0.6.2-IGB-gcc-4.9.4 0.7.15-IGB-gcc-4.9.4 0.7.17-IGB-gcc-4.9.4 0.7.17-IGB-gcc-8.2.0 |
Burrows-Wheeler Aligner (BWA) is an efficient program that aligns relatively short nucleotide sequences against a long reference sequence such as the human genome. |
bx-python | 0.8.13-IGB-gcc-8.2.0-Python-3.7.2 | The bx-python project is a Python library and associated set of scripts for rapid implementation of genome scale analyses. |
byacc | 20170709-IGB-gcc-4.9.4 | Berkeley Yacc (byacc) is generally conceded to be the best yacc variant available.In contrast to bison, it is written to avoid dependencies upon a particular compiler. |
bzip2 | 1.0.6-IGB-gcc-4.9.4 1.0.6-IGB-gcc-8.2.0 |
bzip2 is a freely available, patent free, high-quality data compressor. It typically compresses files to within 10% to 15% of the best available techniques (the PPM family of statistical compressors), whilst being around twice as fast at compression and six times faster at decompression. |
c-ares | c-ares is a C library for asynchronous DNS requests (including name resolves) | |
cactus | 20180705-IGB-gcc-4.9.4-Python-2.7.13 | Cactus is a reference-free whole-genome multiple alignment program. |
cairo | Cairo is a 2D graphics library with support for multiple output devices. Currently supported output targets include the X Window System (via both Xlib and XCB), Quartz, Win32, image buffers, PostScript, PDF, and SVG file output. Experimental backends include OpenGL, BeOS, OS/2, and DirectFB | |
Canu | 1.4-IGB-gcc-4.9.4-Perl-5.24.1 1.5-IGB-gcc-4.9.4-Perl-5.24.1 1.6-IGB-gcc-4.9.4-Perl-5.24.1 1.7-IGB-gcc-4.9.4-Perl-5.24.1 1.7.1-IGB-gcc-4.9.4-Perl-5.24.1 1.8-IGB-gcc-4.9.4-Perl-5.24.1 1.9-IGB-gcc-8.2.0-Perl-5.28.1 2.0-IGB-gcc-8.2.0-Perl-5.28.1 2.1.1-IGB-gcc-8.2.0-Perl-5.28.1 2.2-IGB-gcc-8.2.0-Perl-5.28.1 |
Canu is a fork of the Celera Assembler, designed for high-noise single-molecule sequencing (such as the PacBio RS II or Oxford Nanopore MinION). |
cap-mirseq | 20200817-IGB-gcc-4.9.4 | CAP-miRSeq: A comprehensive analysis pipeline for deep microRNA sequencing |
capnproto | Cap’n Proto is an insanely fast data interchange format and capability-based RPC system. Think JSON, except binary. Or think Protocol Buffers, except faster. | |
cbc_pacbio | beta-IGB-gcc-8.2.0 | Command-line scripts and pipelines to perform downstream analyses of Sequel II subreads |
CCMpred | 20191102-IGB-gcc-4.9.4 | CCMpred is a C implementation of a Markov Random Field pseudo-likelihood maximization for learning protein residue-residue contacts as made popular by Ekeberg et al. [1] and Balakrishnan and Kamisetty [2]. |
CD-HIT | 4.6.6-IGB-gcc-4.9.4 4.8.1-IGB-gcc-8.2.0 |
CD-HIT is a very widely used program for clustering and comparing protein or nucleotide sequences. |
cdbfasta | 20170316-IGB-gcc-4.9.4 20181005-IGB-gcc-8.2.0 |
CDB (Constant DataBase) indexing and retrieval tools for FASTA files |
cddd | 20200130-IGB-gcc-4.9.4-Python-3.6.1 | Continuous and Data-Driven Descriptors |
cellranger | 2.1.0 2.1.1 3.0.0 3.0.1 3.1.0 4.0.0 5.0.0 6.0.1 6.0.2 6.1.1 7.0.0 7.0.1 7.1.0 7.2.0 8.0.0 |
Cell Ranger is a set of analysis pipelines that process Chromium single cell 3 RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. |
cellranger-arc | 1.0.0 2.0.1 2.0.2 |
Cell Ranger ARC is a set of analysis pipelines that process Chromium Single Cell Multiome ATAC + Gene Expression sequencing data to generate a variety of analyses pertaining to gene expression, chromatin accessibility and their linkage. |
cellranger-atac | 1.1.0 1.2.0 2.0.0 2.1.0 |
Cell Ranger ATAC is a set of analysis pipelines that process Chromium Single Cell ATAC data. |
centrifuge | 1.0.3-beta-IGB-gcc-4.9.4 1.0.4-beta-IGB-gcc-4.9.4 |
Centrifuge is a very rapid and memory-efficient system for the classification of DNA sequences from microbial samples, with better sensitivity than and comparable accuracy to other leading systems. |
ceres-solver | 1.14.0-IGB-gcc-4.9.4 1.14.0-IGB-gcc-8.2.0 2.0.0-IGB-gcc-8.2.0 |
Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. |
CheckM | 1.0.7-IGB-gcc-4.9.4-Python-2.7.13 1.1.3-IGB-gcc-8.2.0-Python-3.7.2 1.1.9-IGB-gcc-8.2.0-Python-3.7.2 |
CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. |
CheckM2 | 1.0.1 | Unlike CheckM1, CheckM2 has universally trained machine learning models it applies regardless of taxonomic lineage to predict the completeness and contamination of genomic bins. |
chopchop | 20190211-IGB-gcc-4.9.4-Python-2.7.13 | CHOPCHOP is a python script that allows quick and customizable design of guide RNA. We support selecting target sites for CRISPR/Cas9, CRISPR/Cpf1 or TALEN with wide range of customization. We even support C2c2 for isoform targeting. |
Chromonomer | 1.13-IGB-gcc-8.2.0 | Chromonomer is a program designed to integrate a genome assembly with a genetic map. Chromonomer tries very hard to identify and remove markers that are out of order in the genetic map, when considered against their local assembly order; and to identify scaffolds that have been incorrectly assembled according to the genetic map, and split those scaffolds. |
Circlator | 1.5.1-IGB-gcc-4.9.4-Python-3.6.1 1.5.5-IGB-gcc-8.2.0-Python-3.7.2 |
A tool to circularize genome assemblies. |
Circos | 0.69-4-IGB-gcc-4.9.4-Perl-5.24.1 0.69-9-IGB-gcc-8.2.0-Perl-5.28.1 |
Circos is a software package for visualizing data and information. It visualizes data in a circular layout - this makes Circos ideal for exploring relationships between objects or positions. |
cisgenome | 2.0-IGB-gcc-4.9.4 | An integrated tool for tiling array, ChIP-seq, genome and cis-regulatory element analysis |
clang-format | 15.0.7-IGB-gcc-8.2.0-Python-3.7.2 | Clang-Format is an LLVM-based code formatting tool |
CLASS2 | 2.1.7-IGB-gcc-8.2.0 | CLASS2 is a fast and accurate program for transcript assembly of RNA-seq reads aligned to a reference genome. CLASS2 uses the splice graph model to represent a gene and its splice variants, and a dynamic programming optimization algorithm to score and select a subset of transcripts most likely present in the sample. |
clinker | 0.0.27-IGB-gcc-8.2.0-Python-3.7.2 | clinker is a pipeline for easily generating publication-quality gene cluster comparison figures. |
clipper | 1.2.1-IGB-gcc-4.9.4-Python-2.7.13 | A tool to detect CLIP-seq peaks. |
Clp | Clp (Coin-or linear programming) is an open-source linear programming solver written in C++. | |
CLUMPP | 1.1.2 | CLUMPP is a program that deals with label switching and multimodality problems in population-genetic cluster analyses. |
Clustal-Omega | 1.2.4-IGB-gcc-4.9.4 1.2.4-IGB-gcc-8.2.0 |
Clustal Omega is a multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. Evolutionary relationships can be seen via viewing Cladograms or Phylograms |
ClustalW2 | 2.1-IGB-gcc-4.9.4 | ClustalW2 is a general purpose multiple sequence alignment program for DNA or proteins. - Homepage: http://www.ebi.ac.uk/Tools/msa/clustalw2/ |
clusterflow | 0.5-IGB-gcc-4.9.4-Perl-5.24.1 | Cluster Flow is workflow manager designed to run bioinformatics pipelines. It is operated through a single command cf, which can be used to launch, configure, monitor and cancel pipelines. |
CMake | CMake, the cross-platform, open-source build system. CMake is a family of tools designed to build, test and package software. | |
CNVkit | 0.9.10-IGB-gcc-8.2.0-Python-3.10.1 0.9.8-IGB-gcc-8.2.0-Python-3.7.2 |
A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from high-throughput sequencing. |
CNVnator | 0.3.3-IGB-gcc-4.9.4 0.3.3-IGB-gcc-4.9.4-Python-2.7.13 |
a tool for CNV discovery and genotyping from depth-of-coverage by mapped reads |
cocoapi | 20190218-IGB-gcc-4.9.4-Python-3.6.1 | OCO is a large image dataset designed for object detection, segmentation, person keypoints detection, stuff segmentation, and caption generation. |
colmap | 20210330-IGB-gcc-4.9.4 | COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. |
CONCOCT | 1.0.0-IGB-gcc-4.9.4-Python-3.6.1 1.1.0-IGB-gcc-8.2.0-Python-3.7.2 |
A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads. |
cooler | 0.8.11-IGB-gcc-8.2.0-Python-3.7.2 0.8.2-IGB-gcc-8.2.0-Python-3.7.2 |
Cooler is a support library for a sparse, compressed, binary persistent storage format, also called cooler, used to store genomic interaction data, such as Hi-C contact matrices. |
cooltools | 0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | The tools for your .cools |
COPIES | 20231202-IGB-gcc-8.2.0-Python-3.10.1 | COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites (CRISPR-COPIES) is a user-friendly web application and a command line tool for rapid discovery of neutral integration sites. |
coreutils | 8.28-IGB-gcc-4.9.4 8.28-IGB-gcc-8.2.0 9.1-IGB-gcc-8.2.0 |
The GNU Core Utilities are the basic file, shell and text manipulation utilities of the GNU operating system.These are the core utilities which are expected to exist on every operating system. |
cortex | 1.0.5.21-IGB-gcc-4.9.4 | reference free variant assembly |
CppUnit | CppUnit is the C++ port of the famous JUnit framework for unit testing. | |
crb-blast | 0.6.9-IGB-gcc-4.9.4 | Conditional Reciprocal Best BLAST - high confidence ortholog assignment. CRB-BLAST is a novel method for finding orthologs between one set of sequences and another. This is particularly useful in genome and transcriptome annotation. |
crimap | 2.507-IGB-gcc-8.2.0 | CRI-MAP (version 2.4, by Phil Green et al, 1990) has been used extensively in the past 20 years for genetic linkage analysis of diploid species, and has played a fundamental role in producing genetic linkage maps for humans, rats, mouse, fruit flies, cattle, sheep, pigs, chicken, fish, among many other species. |
cromwell | 39-Java-1.8.0_152 | Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments |
Crossmap | 0.6.5-IGB-gcc-8.2.0-Python-3.7.2 | CrossMap is a program for genome coordinates conversion between different assemblies (such as hg18 (NCBI36) <=> hg19 (GRCh37)). It supports commonly used file formats including BAM, CRAM, SAM, Wiggle, BigWig, BED, GFF, GTF, MAF VCF, and gVCF. |
CUDA | 10.0.130 10.1.105 11.0.3 11.1.0 11.2.2 11.3.0 11.8.0 8.0.61 8.0.61-IGB-gcc-4.9.4 9.0.176 9.1.85 |
CUDA (formerly Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce. CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. |
cuDNN | 5.1-CUDA-8.0.61 5.1-IGB-gcc-4.9.4-CUDA-8.0.61 6.0-IGB-gcc-4.9.4-CUDA-8.0.61 7.0.5-CUDA-9.0.176 7.1.4-CUDA-9.0.176 7.6.1.34-CUDA-10.0.130 8.0.4.30-CUDA-10.1.105 8.0.4.30-CUDA-11.1.0 8.1.1.33-CUDA-11.2.2 8.2.1.32-CUDA-11.3.0 8.9.2.23-CUDA-11.8.0 |
The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. |
Cufflinks | 2.2.1 2.2.1-IGB-gcc-4.9.4-b4fa050 |
Transcript assembly, differential expression, and differential regulation for RNA-Seq |
CUnit | 2.1-3-IGB-gcc-4.9.4 | CUnit is a lightweight system for writing, administering, and running unit tests in C. It provides C programmers a basic testing functionality with a flexible variety of user interfaces. |
cURL | libcurl is a free and easy-to-use client-side URL transfer library, supporting DICT, FILE, FTP, FTPS, Gopher, HTTP, HTTPS, IMAP, IMAPS, LDAP, LDAPS, POP3, POP3S, RTMP, RTSP, SCP, SFTP, SMTP, SMTPS, Telnet and TFTP. libcurl supports SSL certificates, HTTP POST, HTTP PUT, FTP uploading, HTTP form based upload, proxies, cookies, user+password authentication (Basic, Digest, NTLM, Negotiate, Kerberos), file transfer resume, http proxy tunneling and more. | |
cutadapt | 1.14-IGB-gcc-4.9.4-Python-2.7.13 1.17-IGB-gcc-4.9.4-Python-3.6.1 2.10-IGB-gcc-8.2.0-Python-3.7.2 3.7-IGB-gcc-8.2.0-Python-3.7.2 |
Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads. |
cuteSV | 1.0.12-IGB-gcc-8.2.0-Python-3.7.2 | A sensitive, fast and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. |
cytoscape | 3.7.0-Java-1.8.0_152 3.8.2-Java-11.0.5 3.9.1-Java-11.0.5 |
Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. |
damidseq | 1.4-IGB-gcc-4.9.4 | Processing DamID-seq data involves extending single-end reads, aligning the reads to the genome and determining the coverage, similar to processing regular ChIP-seq datasets. |
DamMet | 1.0.2-IGB-gcc-8.2.0 | DamMet is probabilistic model for mapping ancient methylomes using sequencing data underlying an ancient specimen. |
DANPOS | 2.2.2-IGB-gcc-4.9.4-Python-2.7.13-R-3.3.3 | A toolkit for Dynamic Analysis of Nucleosome and Protein Occupancy by Sequencing, version 2 |
DAS_Tool | 1.1.2-IGB-gcc-4.9.4 | DAS Tool is an automated method that integrates the results of a flexible number of binning algorithms to calculate an optimized, non-redundant set of bins from a single assembly. |
DB | 18.1.32-IGB-gcc-4.9.4 | Berkeley DB enables the development of custom data management solutions, without the overhead traditionally associated with such custom projects. |
DeepEC | 20190806-IGB-gcc-4.9.4-Python-3.6.1 | DeepEC |
deeplabcut | 2.1.8.2-IGB-gcc-4.9.4-Python-3.6.1 2.2.1.1-IGB-gcc-8.2.0-Python-3.7.2 |
Markerless pose estimation of user-defined features with deep learning for all animals |
deepTools | 2.5.3-IGB-gcc-4.9.4-Python-2.7.13 3.0.1-IGB-gcc-4.9.4-Python-2.7.13 3.2.1-IGB-gcc-4.9.4-Python-3.6.1 3.5.2-IGB-gcc-4.9.4-Python-3.6.1 3.5.2-IGB-gcc-8.2.0-Python-3.7.2 |
deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. deepTools contains useful modules to process the mapped reads data for multiple quality checks, creating normalized coverage files in standard bedGraph and bigWig file formats, that allow comparison between different files (for example, treatment and control). Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome. |
deep_q_rl | 20160603-IGB-gcc-4.9.4-Python-2.7.13 | This package provides a Lasagne/Theano-based implementation of the deep Q-learning algorithm described in:Playing Atari with Deep Reinforcement Learning Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin RiedmillerandMnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533. |
delly | 0.8.1-IGB-gcc-4.9.4 | Delly is an integrated structural variant (SV) prediction method that can discover, genotype and visualize deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. |
DendroPy | 4.4.0-IGB-gcc-4.9.4-Python-3.6.1 4.4.0-IGB-gcc-8.2.0-Python-3.7.2 |
DendroPy is a Python library for phylogenetic computing. |
deNOPA | 1.0.2-IGB-gcc-4.9.4-Python-2.7.13 | As the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. |
detectron2 | 0.2.1-IGB-gcc-4.9.4-Python-3.6.1 0.2.1-IGB-gcc-8.2.0-Python-3.7.2 0.5-IGB-gcc-8.2.0-Python-3.7.2 0.6-IGB-gcc-8.2.0-Python-3.7.2 |
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms |
DIAMOND | 0.8.38-IGB-gcc-4.9.4 0.9.10-IGB-gcc-4.9.4 0.9.16-IGB-gcc-4.9.4 0.9.22-IGB-gcc-4.9.4 0.9.24-IGB-gcc-4.9.4 0.9.24-IGB-gcc-8.2.0 0.9.36-IGB-gcc-8.2.0 0.9.9-IGB-gcc-4.9.4 2.0.15-IGB-gcc-8.2.0 2.0.6-IGB-gcc-8.2.0 2.0.9-IGB-gcc-8.2.0 |
Accelerated BLAST compatible local sequence aligner |
diffReps | 1.55.6-IGB-gcc-4.9.4-Perl-5.24.1 | ChIP-seq is now widely used to profile the protein DNA interactions on a genome. It is of high interest to compare the differential enrichment of a histone mark or transcription factor between two contrasting conditions, such as disease vs. control. diffReps is developed to serve this purpose. It scans the whole genome using a sliding window, performing millions of statistical tests and report the significant hits. - Homepage: https://github.com/shenlab-sinai/diffreps |
DISCOVARdenovo | 52488-IGB-gcc-4.9.4 | DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants. |
distruct | 2.2-IGB-gcc-4.9.4 | Modified version of the original distruct.py |
DIYABC | 2.1.0 | a user-friendly approach to Approximate Bayesian Computation for inference on population history using molecular markers |
diyabc | 1.1.28 | DIYABC RF V1.0 |
dms-tools2 | 2.6.10-IGB-gcc-8.2.0-Python-3.7.2 | dms_tools2 is a software package for analyzing deep mutational scanning data. It is tailored to analyze libraries created using comprehensive codon mutagenesis of protein-coding genes, and perform analyses that are common to the Bloom lab, |
dorado | 0.6.0 | Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads. |
Doxygen | 1.8.13-IGB-gcc-4.9.4 | Doxygen is a documentation system for C++, C, Java, Objective-C, Python, IDL (Corba and Microsoft flavors), Fortran, VHDL, PHP, C#, and to some extent D. |
drep | 3.2.0-IGB-gcc-8.2.0-Python-3.7.2 | dRep is a python program which performs rapid pair-wise comparison of genome sets. One of it’s major purposes is for genome de-replication, but it can do a lot more. |
ds3_java_cli | 5.1.2 5.1.4 |
Command line utilities for Bioarchive |
ea-utils | 1.04.807-IGB-gcc-4.9.4 | Command-line tools for processing biological sequencing data. Barcode demultiplexing, adapter trimming, etc. Primarily written to support an Illumina based pipeline - but should work with any FASTQs. |
EasyBuild | 4.6.2 | EasyBuild is a software build and installation framework written in Python that allows you to install software in a structured, repeatable and robust way. |
EAT | 1.0.0-IGB-gcc-8.2.0-Python-3.7.2 | Embedding-based annotation transfer (EAT) uses Euclidean distance between vector representations (embeddings) of proteins to transfer annotations from a set of labeled lookup protein embeddings to query protein embeddings. |
EDirect | 20160310-IGB-gcc-4.9.4 | Entrez Direct (EDirect) provides access to the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a UNIX terminal window. |
eeglab | 2021.1-IGB-gcc-8.2.0 | EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. |
eggnog-mapper | 2.1.12-IGB-gcc-8.2.0-Python-3.7.2 | EggNOG-mapper is a tool for fast functional annotation of novel sequences. |
Eigen | Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. | |
eigensoft | 7.2.1-IGB-gcc-4.9.4 | The EIGENSOFT package combines functionality from our population genetics methods (Patterson et al. 2006) and our EIGENSTRAT stratification correction method (Price et al. 2006). |
EMBOSS | 6.6.0-IGB-gcc-4.9.4-Java-1.8.0_121 | EMBOSS, The European Molecular Biology Open Software Suite. EMBOSS is a free Open Source software analysis package specially developed for the needs of the molecular biology (e.g. EMBnet) user community. |
EMIRGE | 0.61.1-IGB-gcc-4.9.4-Python-2.7.13 | EMIRGE reconstructs full length ribosomal genes from short readsequencing data. In the process, it also provides estimates of thesequences abundances. |
epa-ng | 0.3.8-IGB-gcc-8.2.0 | EPA-ng is a complete rewrite of the Evolutionary Placement Algorithm (EPA), previously implemented in RAxML. It uses libpll and pll-modules to perform maximum likelihood-based phylogenetic placement of genetic sequences on a user-supplied reference tree and alignment. |
epic | 0.2.9-IGB-gcc-4.9.4-Python-2.7.13 | epic is a software package for finding medium to diffusely enriched domains in chip-seq data. It is a fast, parallel and memory-efficient implementation of the incredibly popular SICER algorithm. |
epic2 | 0.0.41-IGB-gcc-4.9.4-Python-3.6.1 | epic2 is an ultraperformant reimplementation of SICER. It focuses on speed, low memory overhead and ease of use. |
EVidenceModeler | 1.1.1-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 1.1.1-IGB-gcc-8.2.0-Perl-5.28.1 |
The EVidenceModeler (aka EVM) software combines ab intio gene predictions and protein and transcript alignments into weighted consensus gene structures. EVM provides a flexible and intuitive framework for combining diverse evidence types into a single automated gene structure annotation system. |
evolocity | 0.1-IGB-gcc-8.2.0-Python-3.7.2 | Evolocity is a Python package that implements evolutionary velocity, which constructs landscapes of protein evolution by using the local evolutionary predictions enabled by language models to predict the directionality of evolution and is described in the paper "Evolutionary velocity with protein language models" by Brian Hie, Kevin Yang, and Peter Kim. |
exiftool | 12.54-IGB-gcc-8.2.0-Perl-5.28.1 | Read, Write and Edit Meta Information! |
exonerate | 2.2.0-IGB-gcc-4.9.4 2.2.0-IGB-gcc-8.2.0 |
Exonerate is a generic tool for pairwise sequence comparison. It allows you to align sequences using a many alignment models, either exhaustive dynamic programming or a variety of heuristics. |
ExonOntology | 20171018-IGB-gcc-8.2.0-Perl-5.28.1 | This project consists of a set of scripts that are necessary to perform offline Exon Ontology analyses. Briefly, it can accept a list of genomic sequences as input that represent exons (or parts of exons). The algorithm then retrieves the protein features that are encoded by these DNA sequences. |
expat | Expat is an XML parser library written in C. It is a stream-oriented parser in which an application registers handlers for things the parser might find in the XML document (like start tags) | |
fair-esm | 0.4.0-IGB-gcc-8.2.0-Python-3.7.2 2.0.0-IGB-gcc-8.2.0-Python-3.7.2 |
This repository contains code and pre-trained weights for Transformer protein language models from Facebook AI Research, including our state-of-the-art ESM-1b and MSA Transformer. |
FALCON | 1.8.8-IGB-gcc-4.9.4-Python-2.7.13 | FALCON is a diploid aware genome assembler designed for Pacific Biosciences long read data. |
Falcon2Fastg | 0.3.1-IGB-gcc-4.9.4-Python-2.7.13 | This software converts the results of PacBio assembly using FALCON, to a FASTG graph that can be visualized using Bandage. |
FALCON_unzip | 0.4.0-IGB-gcc-4.9.4-Python-2.7.13 | FALCON-Unzip contains the modules that works with FALCON for full diploid assembly (representing haplotype specific contigs as "haplotigs" as assembly output). |
fast5 | 0.6.2-IGB-gcc-4.9.4 0.6.3-IGB-gcc-4.9.4 0.6.5-IGB-gcc-4.9.4 0.6.5-IGB-gcc-8.2.0 |
A lightweight C++ library for accessing Oxford Nanopore Technologies sequencing data. |
fasta2 | 21.1.1-IGB-gcc-8.2.0 | |
FastANI | 1.32 | FastANI is developed for fast alignment-free computation of whole-genome Average Nucleotide Identity (ANI). ANI is defined as mean nucleotide identity of orthologous gene pairs shared between two microbial genomes. |
FastME | 2.1.6.1-IGB-gcc-4.9.4 2.1.6.3-IGB-gcc-8.2.0 |
FastME provides distance algorithms to infer phylogenies. FastME is based on balanced minimum evolution, which is the very principle of NJ. FastME improves over NJ by performing topological moves using fast, sophisticated algorithms. |
fastmiso | 0.5.4-IGB-gcc-4.9.4-Python-2.7.13 | MISO (Mixture-of-Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. |
fastp | 0.19.5-IGB-gcc-4.9.4 0.19.6-IGB-gcc-4.9.4-7117eba 0.20.0-IGB-gcc-4.9.4 0.23.4 |
A tool designed to provide fast all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported to afford high performance. |
fastPHASE | 20160330 | astPHASE is a program to estimate missing genotypes and unobserved haplotypes. |
FastQ-Screen | 0.14.1-IGB-gcc-8.2.0 | FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect. |
FastQC | 0.11.5-Java-1.8.0_201 0.11.8-Java-1.8.0_152 0.11.9-Java-1.8.0_201 |
FastQC is a quality control application for high throughput sequence data. It reads in sequence data in a variety of formats and can either provide an interactive application to review the results of several different QC checks, or create an HTML based report which can be integrated into a pipeline. |
fastStructure | 1.0-IGB-gcc-4.9.4-Python-2.7.13 | fastStructure is an algorithm for inferring population structure from large SNP genotype data. It is based on a variational Bayesian framework for posterior inference and is written in Python2.x. |
FastTree | 2.1.10-IGB-gcc-4.9.4 2.1.11-IGB-gcc-8.2.0 |
FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences. FastTree can handle alignments with up to a million of sequences in a reasonable amount of time and memory. |
FASTX-Toolkit | 0.0.14-IGB-gcc-4.9.4 | The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing. - Homepage: http://hannonlab.cshl.edu/fastx_toolkit/ |
fdupes | 1.6.1-IGB-gcc-4.9.4 | FDUPES is a program for identifying duplicate files residingwithin specified directories. |
FEELnc | 20180117-IGB-gcc-4.9.4 | This document is intended to give a technical description of the FEELnc pipeline in order to annotate long non-coding RNAs (lncRNAs) based on reconstructed transcripts from RNA-seq data (either with or without a reference genome). |
fermi-lite | Standalone C library for assembling Illumina short reads in small regions. | |
FFmpeg | 3.3-IGB-gcc-4.9.4 4.4-IGB-gcc-8.2.0 |
A complete, cross-platform solution to record, convert and stream audio and video. |
FFTW | FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data. | |
fgbio | 0.6.0-Java-1.8.0_152 | fgbio is a command line toolkit for working with genomic and particularly next generation sequencing data. |
figaro | 1.1.2-IGB-gcc-8.2.0-Python-3.7.2 | An efficient and objective tool for optimizing microbiome rRNA gene trimming parameters. |
file | The file command is a file type guesser, that is, a command-line tool that tells you in words what kind of data a file contains. | |
Filtlong | 0.2.1-IGB-gcc-8.2.0 | Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter. |
fithic | 2.0.7-IGB-gcc-8.2.0-Python-3.7.2 | Fit-Hi-C (or FitHiC) was initially developed by Ferhat Ay, Timothy Bailey, and William Noble January 19th, 2014. |
FLAC | 1.3.1-IGB-gcc-4.9.4 | Programs and libraries for working with Free Lossless Audio Codec (FLAC) files. |
flair | 1.5-IGB-gcc-8.2.0-Python-3.7.2 | FLAIR (Full-Length Alternative Isoform analysis of RNA) for the correction, isoform definition, and alternative splicing analysis of noisy reads. FLAIR has primarily been used for nanopore cDNA, native RNA, and PacBio sequencing reads. |
FLASH2 | 2.2.00-IGB-gcc-4.9.4 | FLASH (Fast Length Adjustment of SHort reads) is an accurate and fast toolto merge paired-end reads that were generated from DNA fragments whoselengths are shorter than twice the length of reads. Merged read pairs resultin unpaired longer reads, which are generally more desired in genomeassembly and genome analysis processes. |
flex | Flex (Fast Lexical Analyzer) is a tool for generating scanners. A scanner, sometimes called a tokenizer, is a program which recognizes lexical patterns in text. | |
flexbar | 3.0.3-IGB-gcc-4.9.4 | The program Flexbar preprocesses high-throughput sequencing data efficiently. It demultiplexes barcoded runs and removes adapter sequences. Moreover, trimming and filtering features are provided. |
Flye | 2.4.2-IGB-gcc-4.9.4-Python-2.7.13 2.7-IGB-gcc-4.9.4-Python-3.6.1 2.7.1-IGB-gcc-8.2.0-Python-3.7.2 2.8.1-IGB-gcc-8.2.0-Python-3.7.2 2.8.2-IGB-gcc-8.2.0-Python-3.7.2 2.9-IGB-gcc-8.2.0-Python-3.7.2 2.9.2-IGB-gcc-8.2.0-Python-3.7.2 |
Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. |
foldseek | 8-ef4e960 | Foldseek enables fast and sensitive comparisons of large structure sets. |
foldx | 5.0 | The FoldX Suite builds on the strong fundament of advanced protein design features, already implemented in the successful FoldX3, and exploits the power of fragment libraries, by integrating in silico digested backbone protein fragments of different lengths. S |
fontconfig | Fontconfig is a library designed to provide system-wide font configuration, customization andapplication access. | |
fpocket | 3.1.3-IGB-gcc-4.9.4 | fpocket is a very fast open source protein pocket detection algorithm based on Voronoi tessellation. |
FragGeneScan | 1.31-IGB-gcc-4.9.4 | FragGeneScan is an application for finding (fragmented) genes in short reads. |
FreeBayes | 1.1.0-IGB-gcc-4.9.4 1.3.4-IGB-gcc-8.2.0 |
a haplotype-based variant detector |
FreeImage | 3.18.0-IGB-gcc-4.9.4 | FreeImage is an Open Source library project for developers who would like to support popular graphicsimage formats like PNG, BMP, JPEG, TIFF and others as needed by today's multimedia applications. FreeImage is easy touse, fast, multithreading safe. |
freetype | FreeType 2 is a software font engine that is designed to be small, efficient, highly customizable, and portable while capable of producing high-quality output (glyph images). It can be used in graphics libraries, display servers, font conversion tools, text image generation tools, and many other products as well. | |
FriBidi | The Free Implementation of the Unicode Bidirectional Algorithm. | |
fseq | 1.84-Java-1.8.0_152 | Tag sequencing using high-throughput sequencing technologies are now regularly employed to identify specific sequence features such as transcription factor binding sites (ChIP-seq) or regions of open chromatin (DNase-seq). |
GAMS | 23.6.5 32.2.0 |
The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming and optimization. It consists of a language compiler and a stable of integrated high-performance solvers. |
gappa | 0.6.1-IGB-gcc-8.2.0 | gappa is a collection of commands for working with phylogenetic data. Its main focus are evolutionary placements of short environmental sequences on a reference phylogenetic tree. Such data is typically produced by tools like EPA-ng, RAxML-EPA or pplacer and usually stored in jplace files. |
GAPPadder | 20170601-IGB-gcc-4.9.4 | GAPPadder is designed for closing gaps on the draft genomes with paired-end reads or mate-paired reads. |
gapseq | 1.1-IGB-gcc-8.2.0 | Informed prediction and analysis of bacterial metabolic pathways and genome-scale networks |
GATK | 3.7-Java-1.8.0_121 3.8-0-Java-1.8.0_121 3.8-0-Java-1.8.0_152 3.8-1-0-Java-1.8.0_152 4.0.4.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1 4.0.9.0-IGB-gcc-4.9.4-Java-1.8.0_152-Python-3.6.1 4.1.4.0-Java-1.8.0_152 4.2.4.1-Java-1.8.0_201 4.2.6.1-Java-1.8.0_201 4.4.0.0-Java-17.0.6 |
Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. |
Gaussian | 16.C.01 | Gaussian 16 is the latest in the Gaussian series of programs. It provides state-of-the-art capabilities for electronic structure modeling. Gaussian 16 is licensed for a wide variety of computer systems. All versions of Gaussian 16 contain every scientific/modeling feature, and none imposes any artificial limitations on calculations other than your computing resources and patience. |
GBS-SNP-CROP | 4.0-IGB-gcc-4.9.4 | The GBS SNP Calling Reference Optional Pipeline (GBS-SNP-CROP) is executed via a sequence of seven Perl scripts that integrate custom parsing and filtering procedures with well-known, vetted bioinformatic tools, giving the user full access to all intermediate files. |
GBSX | 1.3-IGB-gcc-8.2.0-Java-1.8.0_201 | Genotyping by Sequencing is an emerging technology for cost effective variant discovery and genotyping. However, current analysis tools do not fulfill all experimental design and analysis needs. |
GCC | The GNU Compiler Collection includes front ends for C, C++, Objective-C, Fortran, Java, and Ada, as well as libraries for these languages (libstdc++, libgcj,...). | |
GCCcore | The GNU Compiler Collection includes front ends for C, C++, Objective-C, Fortran, Java, and Ada, as well as libraries for these languages (libstdc++, libgcj,...). | |
gce | 1.0.2-IGB-gcc-8.2.0 | GCE (genomic charactor estimator) is a bayes model based method to estimate the genome size, genomic repeat content and the heterozygsis rate of the sequencingsample |
gcta | 1.94.0Beta | GCTA (Genome-wide Complex Trait Analysis) is a software package initially developed to estimate the proportion of phenotypic variance explained by all genome-wide SNPs for a complex trait but has been greatly extended for many other analyses of data from genome-wide association studies (GWASs). |
GD | 2.66-IGB-gcc-4.9.4-Perl-5.24.1 2.73-IGB-gcc-8.2.0-Perl-5.28.1 |
GD.pm - Interface to Gd Graphics Library |
GDAL | 2.3.1-IGB-gcc-4.9.4 3.0.3-IGB-gcc-8.2.0 |
GDAL is a translator library for raster geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents a single abstract data model to the calling application for all supported formats. It also comes with a variety of useful commandline utilities for data translation and processing. |
gdb | 8.0.1-IGB-gcc-4.9.4-Python-2.7.13 | The GNU Project Debugger |
gdc-client | 1.3.0-IGB-gcc-4.9.4-Python-2.7.13 | The GDC Data Transfer Tool Client provides a command-line interface supporting both GDC data downloads and submissions. |
Gdk-Pixbuf | The Gdk Pixbuf is a toolkit for image loading and pixel buffer manipulation. It is used by GTK+ 2 and GTK+ 3 to load and manipulate images. In the past it was distributed as part of GTK+ 2 but it was split off into a separate package in preparation for the change to GTK+ 3. | |
geneclass | 2.0 | GeneClass2 is a program employing multilocus genotypes to select or exclude populations as origins of individuals (Assignment and Migrants Detection). This is a completely rewritten new version that includes pop100gene features. |
GeneMark-ES | 4.33-IGB-gcc-4.9.4-Perl-5.24.1 4.33-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 4.62-IGB-gcc-8.2.0-Perl-5.28.1 |
Novel genomes can be analyzed by the program GeneMark-ES utilizing unsupervised training. |
GeneMarkS | 4.30-IGB-gcc-4.9.4 | GeneMarkS: a self-training method for prediction of gene starts in microbial genomes |
GeneMarkS-T | 5.1 | |
gengetopt | Gengetopt is a tool to write command line option parsing code for C programs. - Homepage: https://www.gnu.org/software/gengetopt/gengetopt.html | |
GenomeThreader | 1.7.1 | GenomeThreader is a software tool to compute gene structure predictions. The gene structure predictions are calculated using a similarity-based approach where additional cDNA/EST and/or protein sequences are used to predict gene structures via spliced alignments. GenomeThreader was motivated by disabling limitations in GeneSeqer, a popular gene prediction program which is widely used for plant genome annotation. |
genometools | 1.5.10-IGB-gcc-4.9.4 | The GenomeTools genome analysis system is a free collection of bioinformatics tools (in the realm of genome informatics) combined into a single binary named gt. It is based on a C library named “libgenometools” which consists of several modules. |
GenomicConsensus | 2.3.3 | The GenomicConsensus package provides the variantCaller tool, which allows you to apply the Quiver or Arrow algorithm to mapped PacBio reads to get consensus and variant calls. |
genrich | 0.6-IGB-gcc-8.2.0 | Genrich is a peak-caller for genomic enrichment assays (e.g. ChIP-seq, ATAC-seq). It analyzes alignment files generated following the assay and produces a file detailing peaks of significant enrichment. |
geos | 3.7.1-IGB-gcc-4.9.4 | GEOS (Geometry Engine - Open Source) is a C++ port of the JTS Topology Suite |
GEOS | GEOS (Geometry Engine - Open Source) is a C++ port of the Java Topology Suite (JTS) | |
gettext | GNU gettext is an important step for the GNU Translation Project, as it is an asset on which we maybuild many other steps. This package offers to programmers, translators, and even users, a well integrated set of toolsand documentation | |
gevalt | 2.0-Java-1.8.0_152 | GEVALT (GEnotype Visualization and ALgorithmic Tool) is designed to simplify and expedite the process of genotype analysis and disease association tests by providing a common interface to several common tasks relating to such analyses. |
gfatools | 0.4-IGB-gcc-4.9.4 | gfatools is a set of tools for manipulating sequence graphs in the GFA or the rGFA format. It has implemented parsing, subgraph and conversion to FASTA/BED. |
gff3sort | 1.0.0-IGB-gcc-4.9.4-Perl-5.24.1 | A Perl Script to sort gff3 files and produce suitable results for tabix tools |
gffcompare | 0.10.6-IGB-gcc-4.9.4 | The program gffcompare can be used to compare, merge, annotate and estimate accuracy of one or more GFF files (the query files), when compared with a reference annotation (also provided as GFF). |
gffread | ba7535f-IGB-gcc-4.9.4 | The program gffread can be used to validate, filter, convert and perform various other operations on GFF files |
gffutils | 0.10.1-IGB-gcc-8.2.0-Python-3.7.2 0.11.1-IGB-gcc-8.2.0-Python-3.7.2 |
|
gflags | 2.2.2-IGB-gcc-4.9.4 2.2.2-IGB-gcc-8.2.0 |
The gflags package contains a C++ library that implements commandline flagsprocessing. It includes built-in support for standard types such as stringand the ability to define flags in the source file in which they are used. |
GFOLD | 1.1.4-IGB-gcc-4.9.4 | GFOLD stands for Generalized FOLD change for ranking differentially expressed genes from RNA-seq data. GFOLD is especially useful when no replicate is available. GFOLD generalizes the fold change by considering the posterior distribution of log fold change, such that each gene is assigned a reliable fold change. |
GhostScript | 9.21-IGB-gcc-4.9.4 9.55.0-IGB-gcc-8.2.0 |
Ghostscript is a versatile processor for PostScript data with the ability to render PostScript to different targets. It used to be part of the cups printing stack, but is no longer used for that. |
git | 2.28.0-IGB-gcc-8.2.0 2.9.5-IGB-gcc-4.9.4 |
Git is a free and open source distributed version control system designedto handle everything from small to very large projects with speed and efficiency. |
git-lfs | 2.2.1-IGB-gcc-4.9.4 | Git Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server like GitHub.com or GitHub Enterprise. |
glew | 2.1.0-IGB-gcc-4.9.4 | The OpenGL Extension Wrangler Library (GLEW) is a cross-platform open-sourceC/C++ extension loading library. GLEW provides efficient run-time mechanismsfor determining which OpenGL extensions are supported on the target platform. |
GLib | GLib is one of the base libraries of the GTK+ project | |
GLIMMER | 3.02b-IGB-gcc-4.9.4 | Glimmer is a system for finding genes in microbial DNA, especiallythe genomes of bacteria, archaea, and viruses. |
GlimmerHMM | 3.0.4-IGB-gcc-4.9.4 | GlimmerHMM is a new gene finder based on a Generalized Hidden Markov Model. Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models. |
globus-cli | 3.10.1-IGB-gcc-8.2.0-Python-3.7.2 3.18.0-IGB-gcc-8.2.0-Python-3.7.2 |
The CLI provides an interface to Globus services from the shell, and is suited to both interactive and simple scripting use cases. |
glog | 0.4.0-IGB-gcc-4.9.4 0.5.0-IGB-gcc-8.2.0 |
A C++ implementation of the Google logging module. |
glproto | X protocol and ancillary headers | |
GMAP | 2018-05-30-IGB-gcc-4.9.4 2020-06-04-IGB-gcc-8.2.0 |
A Genomic Mapping and Alignment Program for mRNA and EST Sequences, andGSNAP: Genomic Short-read Nucleotide Alignment Program |
GMAP-GSNAP | 2017-11-15-IGB-gcc-4.9.4 | GMAP: A Genomic Mapping and Alignment Program for mRNA and EST Sequences GSNAP: Genomic Short-read Nucleotide Alignment Program |
GMP | GMP is a free library for arbitrary precision arithmetic, operating on signed integers, rational numbers, and floating point numbers. | |
GMTK | 1.4.4-IGB-gcc-4.9.4 | The Graphical Models Toolkit (GMTK) is an open source, publicly available toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). GMTK can be used for applications and research in speech and language processing, bioinformatics, activity recognition, and any time series application. GMTK has many features, including exact and approximate inference; a large variety of built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors; arbitrary order embedded Markov chains; a GUI-based graph viewer; flexible feature-file support and processing tools (supporting pfiles, HTK files, ASCII/binary, and HDF5 files); and both offline and streaming online inference methods that can be used for both parameter learning and prediction. More information is available in the documentation. All in all, GMTK offers a flexible, concise, and expressive probabilistic modeling framework with which one may rapidly specify a vast collection of temporal statistical models. |
gnuplot | 4.6.7-IGB-gcc-4.9.4 4.6.7-IGB-gcc-8.2.0 5.0.6-IGB-gcc-4.9.4 |
Portable interactive, function plotting utility |
GObject-Introspection | GObject introspection is a middleware layer between C libraries (using GObject) and language bindings. The C library can be scanned at compile time and generate a metadata file, in addition to the actual native C library. Then at runtime, language bindings can read this metadata and automatically provide bindings to call into the C library. | |
gofasta | 0.0.3 | Gofasta uses a slightly modified version of the bit-level coding scheme for nucleotides by Emmanuel Paradis (described here, and implemented in the R package ape). |
gompi | GNU Compiler Collection (GCC) based compiler toolchain, including OpenMPI for MPI support. | |
gossamer | 20170105-IGB-gcc-4.9.4 | The gossamer bioinformatics suite contains goss, gossple, xenome, and electus |
gperf | 3.1-IGB-gcc-4.9.4 3.1-IGB-gcc-8.2.0 |
GNU gperf is a perfect hash function generator. For a given list of strings, it produces a hash function and hash table, in form of C or C++ code, for looking up a value depending on the input string. The hash function is perfect, which means that the hash table has no collisions, and the hash table lookup needs a single string comparison only. |
Gradle | 4.7 | Complete Gradle install.From mobile apps to microservices, from small startups to big enterprises,Gradle helps teams build, automate and deliver better software, faster. |
GraphicsMagick | 1.3.26-IGB-gcc-4.9.4 | GraphicsMagick is the swiss army knife of image processing. |
graphlan | 6ca8735-IGB-gcc-4.9.4-Python-2.7.13 | GraPhlAn is a software tool for producing high-quality circular representations of taxonomic and phylogenetic trees. It focuses on concise, integrative, informative, and publication-ready representations of phylogenetically- and taxonomically-driven investigation. |
graphtyper | 1.3-IGB-gcc-4.9.4 | Graphtyper is a highly scalable genotyping software. It represents a reference genome and known variants of a genomic region using an acyclic mathematical graph structure (a "pangenome reference"), which high-throughput sequence reads are re-aligned to for the purpose of discovering and genotyping SNPs and small indels. |
graphviz | 2.40.1-IGB-gcc-4.9.4 2.40.1-IGB-gcc-4.9.4-Python-2.7.13 |
Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains. |
Grinder | 0.5.4-IGB-gcc-4.9.4-Perl-5.24.1 | Grinder is a versatile open-source bioinformatic tool to create simulated omic shotgun and amplicon sequence libraries for all main sequencing platforms. |
GROMACS | 2021.2-IGB-gcc-8.2.0 2021.2-IGB-gcc-8.2.0-CUDA-11.1.0 |
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. |
GSL | The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. | |
gsutil | 4.52-IGB-gcc-4.9.4-Python-3.6.1 | gsutil is a Python application that lets you access Cloud Storage from the command line. |
GTDBTk | 1.5.0 2.1.1 2.3.0 |
GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB |
gtest | 1.8.0-IGB-gcc-4.9.4 | Googles C++ test framework |
GTK+ | 2.24.31-IGB-gcc-4.9.4-Python-2.7.13 2.24.31-IGB-gcc-4.9.4-Python-3.6.1 2.24.31-IGB-gcc-8.2.0-Python-3.7.2 |
The GTK+ 2 package contains libraries used for creating graphical user interfaces for applications. |
gtool | 0.7.5 | GTOOL is a program for transforming sets of genotype data for use with the programs SNPTEST and IMPUTE. |
GUIDANCE | 2.02-IGB-gcc-4.9.4-Perl-5.24.1 | GUIDANCE is meant to be used for weighting, filtering or masking unreliably aligned positions in sequence alignments before subsequent analysis. For example, align codon sequences (nucleotide sequences that code for proteins) using PAGAN, remove columns with low GUIDANCE scores, and use the remaining alignment to infer positive selection using the branch-site dN/dS test. Other analyses where GUIDANCE filtering could be useful include phylogeny reconstruction, reconstruction of the history of specific insertion and deletion events, inference of recombination events, etc. - Homepage: http://guidance.tau.ac.il/ver2/ |
guideseq | 20190913-IGB-gcc-8.2.0-Python-3.7.2 | The guideseq package implements our data preprocessing and analysis pipeline for GUIDE-Seq data. It takes raw sequencing reads (FASTQ) and a parameter manifest file (.yaml) as input and produces a table of annotated off-target sites as output. |
guppy | 2.1.3 2.2.2 2.3.1 2.3.7 3.0.3 3.1.5 3.2.2 3.4.3 |
Guppy is a stand-alone data bioinformatic toolkit that provides Oxford Nanopore Technologies basecalling algorithm, along with a number of post-processing tools. |
guppy-gpu | 2.3.1 2.3.5 2.3.7 3.0.3 3.1.5 3.2.2 3.4.3 3.5.2 3.6.0 4.0.11 4.0.15 4.2.2 4.5.3 5.0.16 |
Guppy is a stand-alone data bioinformatic toolkit that provides Oxford Nanopore Technologies basecalling algorithm, along with a number of post-processing tools. |
Gurobi | 7.5.2 | The Gurobi Optimizer was designed from the ground up to be the fastest, most powerful solver available for your LP, QP, QCP, and MIP (MILP, MIQP, and MIQCP) problems. |
GUSHR | 20200928-Java-1.8.0_201 | Assembly-free construction of UTRs from short read RNA-Seq data on the basis of coding sequence annotation. |
gzrt | 0.8-IGB-gcc-8.2.0 | So you thought you had your files backed up - until it came time to restore. Then you found out that you had bad sectors and you've lost almost everything because gzip craps out 10% of the way through your archive. The gzip Recovery Toolkit has a program - gzrecover - that attempts to skip over bad data in a gzip archive. This saved me from exactly the above situation. Hopefully it will help you as well. |
h2o | 3.14.0.3-IGB-gcc-4.9.4-R-3.3.3 | R scripting functionality for H2O, the open source math engine for big data that computes parallel distributed machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments. |
h5py | a toolkit for working with nanopore sequencing data from Oxford Nanopore. | |
hapflk | 1.4-IGB-gcc-4.9.4-Python-3.6.1 | hapflk is a software implementing the hapFLK and FLK tests for the detection of selection signatures based on multiple population genotyping data. |
HAPO-G | 1.2-IGB-gcc-8.2.0 | Hapo-G (pronounced like apogee) is a tool that aims to improve the quality of genome assemblies by polishing the consensus with accurate reads. |
HarfBuzz | HarfBuzz is an OpenType text shaping engine. | |
HDF5 | 1.8.12-IGB-gcc-4.9.4 1.8.18-IGB-gcc-4.9.4 1.8.18-IGB-gcc-8.2.0 |
HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. |
help2man | help2man produces simple manual pages from the --help and --version output of other commands. | |
hhsuite | 3.2.0 3.3.0-IGB-gcc-8.2.0 |
The HH-suite is an open-source software package for sensitive protein sequence searching based on the pairwise alignment of hidden Markov models (HMMs). |
HiC-Pro | 3.1.0-IGB-gcc-8.2.0-Python-3.7.2 | HiC-Pro was designed to process Hi-C data, from raw fastq files (paired-end Illumina data) to the normalized contact maps. Since version 2.7.0, HiC-Pro supports the main Hi-C protocols, including digestion protocols as well as protocols that do not require restriction enzyme such as DNase Hi-C. |
HiCExplorer | 2.2.1.1-IGB-gcc-8.2.0-Python-3.7.2 3.7.2-IGB-gcc-8.2.0-Python-3.7.2 |
HiCExplorer addresses the common tasks of Hi-C data analysis from processing to visualization. |
hifiasm | 0.13-IGB-gcc-8.2.0 0.14.2-IGB-gcc-8.2.0 0.15-IGB-gcc-8.2.0 0.16.1-IGB-gcc-8.2.0 0.18.1-IGB-gcc-8.2.0 0.19.5-IGB-gcc-8.2.0 0.19.6-IGB-gcc-8.2.0 0.5-IGB-gcc-8.2.0 |
Hifiasm is a fast haplotype-resolved de novo assembler for PacBio Hifi reads. Unlike most existing assemblers, hifiasm starts from uncollapsed genome. Thus, it is able to keep the haplotype information as much as possible. |
hifiasm-meta | 0.3-IGB-gcc-8.2.0 | A hifiasm fork for metagenome assembly using Hifi reads |
higlass-python | 0.4.4-IGB-gcc-8.2.0-Python-3.7.2 | Python bindings to the HiGlass for tile serving, view config generation, and Jupyter Notebook + Lab integration. |
HISAT2 | 2.0.5-IGB-gcc-4.9.4 2.1.0-IGB-gcc-4.9.4 2.2.0-IGB-gcc-4.9.4 2.2.1-IGB-gcc-8.2.0-Python-3.7.2 |
HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) against the general human population (as well as against a single reference genome). |
HISAT2-3N | 20221013-IGB-gcc-8.2.0-Python-3.7.2 | HISAT-3N (hierarchical indexing for spliced alignment of transcripts - 3 nucleotides) is designed for nucleotide conversion sequencing technologies and implemented based on HISAT2. |
HMMER | 2.3.2-IGB-gcc-4.9.4 2.3.2-IGB-gcc-8.2.0 3.1b2-IGB-gcc-4.9.4 3.2.1-IGB-gcc-4.9.4 3.3.1-IGB-gcc-8.2.0 |
HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST. |
HOMER | 4.9.1-IGB-gcc-4.9.4-Perl-5.24.1 | HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of tools for Motif Discovery and ChIP-Seq analysis. It is a collection of command line programs for unix-style operating systems written in mostly perl and c++. Homer was primarily written as a de novo motif discovery algorithm that is well suited for finding 8-12 bp motifs in large scale genomics data. |
HOPS | 0.33 | HOPS is a java pipeline which focuses on screening MALT data for the presence of a user-specified list of target species. The pipeline essentially exists to make it easier to use MALT and MaltExtract in unison. |
htop | 2.2.0-IGB-gcc-4.9.4 | This is htop, an interactive process viewer for Unix systems. It is a text-mode application (for console or X terminals) and requires ncurses. |
HTSeq | 0.12.4-IGB-gcc-8.2.0-Python-3.7.2 0.9.0-IGB-gcc-4.9.4-Python-2.7.13 0.9.1-IGB-gcc-4.9.4-Python-2.7.13 |
A framework to process and analyze data from high-throughput sequencing (HTS) assays |
HTSlib | 1.10.2-IGB-gcc-8.2.0 1.11-IGB-gcc-8.2.0 1.12-IGB-gcc-8.2.0 1.17-IGB-gcc-8.2.0 1.4-IGB-gcc-4.9.4 1.5-IGB-gcc-4.9.4 1.9-IGB-gcc-4.9.4 1.9-IGB-gcc-8.2.0 |
A C library for reading/writing high-throughput sequencing data. This package includes the utilities bgzip and tabix |
humann | 3.0.1-IGB-gcc-8.2.0-Python-3.7.2 3.1.1-IGB-gcc-8.2.0-Python-3.7.2 3.6-IGB-gcc-8.2.0-Python-3.7.2 3.7-IGB-gcc-8.2.0-Python-3.7.2 |
HUMAnN (the HMP Unified Metabolic Analysis Network) is a method for efficiently and accurately profiling the abundance of microbial metabolic pathways and other molecular functions from metagenomic or metatranscriptomic sequencing data. |
HUMAnN2 | 0.11.1-IGB-gcc-4.9.4-Python-3.6.1 0.11.2-IGB-gcc-4.9.4-Python-3.6.1 |
HUMAnN is a pipeline for efficiently and accurately profiling the presence/absence and abundance of microbial pathways in a community from metagenomic or metatranscriptomic sequencing data (typically millions of short DNA/RNA reads). |
hwloc | The Portable Hardware Locality (hwloc) software package provides a portable abstraction(across OS, versions, architectures, ...) of the hierarchical topology of modern architectures, includingNUMA memory nodes, sockets, shared caches, cores and simultaneous multithreading. It also gathers varioussystem attributes such as cache and memory information as well as the locality of I/O devices such asnetwork interfaces, InfiniBand HCAs or GPUs. It primarily aims at helping applications with gatheringinformation about modern computing hardware so as to exploit it accordingly and efficiently. | |
hypo | 1.0.3-IGB-gcc-8.2.0 | HyPo--a Hybrid Polisher-- utilises short as well as long reads within a single run to polish a long reads assembly of small and large genomes. I |
iced | 0.5.10-IGB-gcc-8.2.0-Python-3.7.2 | ICE normalization |
icorn | 0.97-IGB-gcc-4.9.4 | Inputs: Reference Sequence (FASTA) & reads (FASTQ) & information of short reads |
idba-ud | 1.1.3-IGB-gcc-4.9.4 | IDBA-UD is a iterative De Bruijn Graph De Novo Assembler for Short Reads Sequencing data with Highly Uneven Sequencing Depth. |
IDR | 2.0.4-IGB-gcc-4.9.4-Python-3.6.1 | The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility. Unlike the usual scalar measures of reproducibility, the IDR approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. In laymans terms, the IDR method compares a pair of ranked lists of identifications (such as ChIP-seq peaks). |
IGB-gcc | GNU Compiler Collection (GCC) based compiler toolchain, including OpenMPI for MPI support, OpenBLAS (BLAS and LAPACK support), FFTW and ScaLAPACK. | |
igv | 2.4.4-Java-1.8.0_152 2.7.2-Java-11.0.5 2.8.0-Java-11.0.5 snapshot-Java-11.0.5 |
The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations. |
IllinoisDataBank | 1.0-IGB-gcc-4.9.4-Python-3.6.1 | The Illinois Data Bank is a public access repository for publishing research data from the University of Illinois at Urbana-Champaign |
IM-TORNADO | 2.0.3.3-IGB-gcc-4.9.4-Python-2.7.13 | A pipeline for 16S reads from paired-end libraries |
ImageMagick | 6.9.11-58-IGB-gcc-4.9.4 7.0.5-5-IGB-gcc-4.9.4 |
ImageMagick is a software suite to create, edit, compose, or convert bitmap images |
impute2 | 2.3.2 | IMPUTE2 is freely available for academic use. To see rules for non-academic use, please read the LICENCE file, which is included with each software download. |
Infernal | 1.1.2-IGB-gcc-4.9.4 | Infernal,INFERence of RNA ALignment, is for searching DNA sequence databases for RNA structure and sequence similarities. |
InterProScan | 5.27-66.0-IGB-gcc-4.9.4-Java-1.8.0_152 5.28-67.0-IGB-gcc-4.9.4-Java-1.8.0_152 5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_152 5.33-72.0-IGB-gcc-4.9.4-Java-1.8.0_201 5.47-82.0-IGB-gcc-8.2.0-Java-15.0.1 5.56-89.0-IGB-gcc-8.2.0-Java-15.0.1 |
InterProScan is the software package that allows sequences (protein and nucleic) to be scanned against InterPros signatures. Signatures are predictive models, provided by several different databases, that make up the InterPro consortium. |
intervene | 0.6.5 | a tool for intersection and visualization of multiple genomic region sets |
ior | 3.0.1-IGB-gcc-4.9.4 | Parallel filesystem I/O benchmark |
ipyrad | 0.9.57 | Welcome to ipyrad, an interactive assembly and analysis toolkit for restriction-site associated DNA (RAD-seq) and related data types. Please explore the documentation to find out more about the features of ipyrad. |
IPython | 5.3.0-IGB-gcc-4.9.4-Python-3.6.1 | IPython provides a rich architecture for interactive computing with: Powerful interactive shells (terminal and Qt-based). A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. |
iRep | 20191228-IGB-gcc-8.2.0-Python-3.7.2 | iRep is a method for determining replication rates for bacteria from single time point metagenomics sequencing and draft-quality genomes. |
IRFinder | 1.3.1-IGB-gcc-8.2.0 | Detecting intron retention from RNA-Seq experiments |
isoseq3 | 3.7.0-0 | IsoSeq v3 contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq GUI-based analysis application. |
ITSx | 1.1.1-IGB-gcc-4.9.4 | TSx: Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for use in environmental sequencing |
ivar | 1.3.1-IGB-gcc-8.2.0 | iVar is a computational package that contains functions broadly useful for viral amplicon-based sequencing. Additional tools for metagenomic sequencing are actively being incorporated into iVar. |
Jalview | 2.11.0-Java-1.8.0_152 | |
jansson | Jansson is a C library for encoding, decoding and manipulating JSON data. | |
Jasmine | 1.1.5 | This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. |
JasPer | 1.900.1-IGB-gcc-4.9.4 2.0.10-IGB-gcc-4.9.4 2.0.14-IGB-gcc-8.2.0 |
The JasPer Project is an open-source initiative to provide a free software-based reference implementation of the codec specified in the JPEG-2000 Part-1 standard. |
Java | 1.8.0_121 1.8.0_152 1.8.0_201 11.0.5 15.0.1 17.0.6 |
Java Platform, Standard Edition (Java SE) lets you develop and deployJava applications on desktops and servers. |
JavaFX | 21 | JavaFX is an open source, next generation client application platform for desktop, mobile and embedded systems built on Java. |
jax-unirep | 0.9-IGB-gcc-4.9.4-Python-3.6.1 | Reimplementation of the UniRep protein featurization model in JAX. |
Jellyfish | 1.1.12-IGB-gcc-4.9.4 1.1.12-IGB-gcc-8.2.0 2.2.10-IGB-gcc-8.2.0 2.2.6-IGB-gcc-4.9.4 2.3.0-IGB-gcc-8.2.0 |
Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. |
jemalloc | jemalloc is a general purpose malloc(3) implementation that emphasizes fragmentation avoidance and scalable concurrency support. | |
JsonCpp | JsonCpp is a C++ library that allows manipulating JSON values, including serialization and deserialization to and from strings. It can also preserve existing comment in unserialization/serialization steps, making it a convenient format to store user input files. | |
Juicebox | 1.11.08-Java-1.8.0_201 | Juicebox is visualization software for Hi-C data. This distribution includes the source code for Juicebox, Juicer Tools, and Assembly Tools. Download Juicebox here, or use Juicebox on the web |
Juicer | 1.6.0-IGB-gcc-8.2.0 | Juicer is a platform for analyzing kilobase resolution Hi-C data. In this distribution, we include the pipeline for generating Hi-C maps from fastq raw data files and command line tools for feature annotation on the Hi-C maps. |
juicer_tools | 1.22.01-Java-1.8.0_201 | |
JUnit | A programmer-oriented testing framework for Java. | |
jupyter | 1.0.0-IGB-gcc-4.9.4-Python-3.6.1 | Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. |
jupyterlab | 2.2.9-IGB-gcc-8.2.0-Python-3.7.2 3.5.0-IGB-gcc-8.2.0-Python-3.10.1 |
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. |
kallisto | 0.43.1-IGB-gcc-4.9.4 0.44.0-IGB-gcc-4.9.4 |
kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. |
KAT | 2.4.1-IGB-gcc-4.9.4 | KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. |
kentUtils | 353-IGB-gcc-4.9.4 | UCSC genome browser kent bioinformatic utilities |
Keras | 2.0.6-IGB-gcc-4.9.4-Python-2.7.13 2.0.8-IGB-gcc-4.9.4-Python-3.6.1 2.1.2-IGB-gcc-4.9.4-Python-3.6.1 2.1.5-IGB-gcc-4.9.4-Python-3.6.1 2.11.0-IGB-gcc-8.2.0-Python-3.7.2 2.2.0-IGB-gcc-4.9.4-Python-3.6.1 2.2.2-IGB-gcc-4.9.4-Python-3.6.1 2.2.4-IGB-gcc-4.9.4-Python-3.6.1 2.3.1-IGB-gcc-4.9.4-Python-3.6.1 2.3.1-IGB-gcc-4.9.4-Python-3.6.1-TF-2.0.3 |
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. |
keras-rl | 0.3.0-IGB-gcc-4.9.4-Python-2.7.13 | keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. |
KING | 2.3.2-IGB-gcc-8.2.0 | KING is a toolset that makes use of high-throughput SNP data typically seen in a genome-wide association study (GWAS) or a sequencing project. Applications of KING include family relationship inference and pedigree error checking, quality control, population substructure identification, forensics, gene mapping, etc. |
kma | 1.3.24-IGB-gcc-8.2.0 | KMA is a mapping method designed to map raw reads directly against redundant databases, in an ultra-fast manner using seed and extend. |
KMC | 3.1.1 3.2.1 |
KMC is a disk-based programm for counting k-mers from (possibly gzipped) FASTQ/FASTA files. The homepage of the KMC project is http://sun.aei.polsl.pl/kmc |
kmerfreq | 4.0-IGB-gcc-8.2.0 | kmerfreq count K-mer (with size K) frequency from the input sequence data, typically sequencing reads data, and reference genome data is also applicable. |
KneadData | 0.10.0-IGB-gcc-8.2.0-Python-3.7.2 0.12.0-IGB-gcc-8.2.0-Python-3.7.2 0.6.1-IGB-gcc-4.9.4-Python-3.6.1 0.8.0-IGB-gcc-8.2.0-Python-3.7.2 |
KneadData is a tool designed to perform quality control on metagenomic sequencing data, especially data from microbiome experiments. In these experiments, samples are typically taken from a host in hopes of learning something about the microbial community on the host. However, metagenomic sequencing data from such experiments will often contain a high ratio of host to bacterial reads. This tool aims to perform principled in silico separation of bacterial reads from these contaminant reads, be they from the host, from bacterial 16S sequences, or other user-defined sources. |
Kraken | 1.0-IGB-gcc-4.9.4 1.1.1-IGB-gcc-8.2.0 |
Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. |
Kraken2 | 2.0.8-beta-IGB-gcc-4.9.4 2.1.1-IGB-gcc-8.2.0 2.1.2-IGB-gcc-8.2.0 |
Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. |
KrakenTools | 1.2-IGB-gcc-8.2.0-Python-3.7.2 | KrakenTools is a suite of scripts to be used alongside the Kraken, KrakenUniq, Kraken 2, or Bracken programs. These scripts are designed to help Kraken users with downstream analysis of Kraken results. |
Krona | 2.7-IGB-gcc-4.9.4-Perl-5.24.1 | Interactively explore metagenomes and more from a web browser. |
kyoto | 20170410-IGB-gcc-4.9.4 | Kyoto Tycoon is a lightweight network server on top of the Kyoto Cabinet key-value database, built for high-performance and concurrency. Some of its features include: |
LAME | 3.99.5-IGB-gcc-4.9.4 | LAME is a high quality MPEG Audio Layer III (MP3) encoder licensed under the LGPL. |
Lasagne | 20170606-IGB-gcc-4.9.4-Python-2.7.13 | Lasagne is a lightweight library to build and train neural networks in Theano. - Homepage: http://lasagne.readthedocs.io/en/latest/index.html |
last | 1257-IGB-gcc-8.2.0 | LAST is designed for moderately large data (e.g. genomes, DNA reads,proteomes). |
LASTZ | 1.04.00-IGB-gcc-4.9.4 | LASTZ is a program for aligning DNA sequences, a pairwise aligner. Originally designed to handle sequences the size of human chromosomes and from different species, it is also useful for sequences produced by NGS sequencing technologies such as Roche 454. - Homepage: http://www.bx.psu.edu/~rsharris/lastz/ |
lbzip2 | 2.5-IGB-gcc-4.9.4 | lbzip2 is a free, multi-threaded compression utility with support for bzip2 compressed file format. |
LEfSe | 20180219-IGB-gcc-4.9.4-Python-2.7.13 | LEfSe |
Lep-MAP3 | 20221128-Java-15.0.1 | Lep-MAP3 (LM3) is a novel linkage map construction software suite. It can handle millions of markers and thousands of individuals possibly on multiple families. Input genotype data can be from genome sequencing (RADseq or whole genome sequencing), SNP assay, microsatellites or any mixture of them. |
lftp | 4.9.2-IGB-gcc-8.2.0 | FTP is a sophisticated file transfer program supporting a number of network protocols (ftp, http, sftp, fish, torrent). |
libaec | 1.0.6-IGB-gcc-8.2.0 | Libaec provides fast lossless compression of 1 up to 32 bit wide signed or unsigned integers(samples). The library achieves best results for low entropy data as often encountered in space imaginginstrument data or numerical model output from weather or climate simulations. While floating point representationsare not directly supported, they can also be efficiently coded by grouping exponents and mantissa. |
libBigWig | A C library for reading/parsing local and remote bigWig and bigBed files. While Kent's source code is free to use for these purposes | |
libcerf | libcerf is a self-contained numeric library that provides an efficient and accurate implementation of complex error functions, along with Dawson, Faddeeva, and Voigt functions. | |
libdap | A C++ SDK which contains an implementation of DAP 2.0 and the development versions of DAP3, up to 3.4. This includes both Client- and Server-side support classes. | |
libdrm | Direct Rendering Manager runtime library. | |
libevent | The libevent API provides a mechanism to execute a callback function when a specific event occurs on a file descriptor or after a timeout has been reached. Furthermore, libevent also support callbacks due to signals or regular timeouts. | |
libfaketime | libfaketime intercepts various system calls that programs use to retrieve thecurrent date and time. It then reports modified (faked) dates and times (asspecified by you, the user) to these programs. This means you can modify thesystem time a program sees without having to change the time system-wide. | |
libffi | The libffi library provides a portable, high level programming interface to various callingconventions. This allows a programmer to call any function specified by a call interface description at run-time. | |
libgd | GD is an open source code library for the dynamic creation of images by programmers. | |
libgdiplus | An Open Source implementation of the GDI+ API | |
libglvnd | libglvnd is a vendor-neutral dispatch layer for arbitrating OpenGL API calls between multiple vendors. | |
libgpuarray | 0.6.5-IGB-gcc-4.9.4 | Make a common GPU ndarray(n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. - Homepage: http://deeplearning.net/software/libgpuarray/ |
libgtextutils | ligtextutils is a dependency of fastx-toolkit and is provided via the same upstream - Homepage: http://hannonlab.cshl.edu/fastx_toolkit/ | |
libharu | libHaru is a free, cross platform, open source library for generating PDF files. | |
libjpeg-turbo | libjpeg-turbo is a fork of the original IJG libjpeg which uses SIMD to accelerate baseline JPEGcompression and decompression. libjpeg is a library that implements JPEG image encoding, decoding and transcoding. | |
libpciaccess | Generic PCI access library. | |
libpng | libpng is the official PNG reference library | |
libpthread-stubs | The X protocol C-language Binding (XCB) is a replacement for Xlib featuring a small footprint,latency hiding, direct access to the protocol, improved threading support, and extensibility. | |
libreadline | The GNU Readline library provides a set of functions for use by applications that allow users to edit command lines as they are typed in. Both Emacs and vi editing modes are available. The Readline library includes additional functions to maintain a list of previously-entered command lines, to recall and perhaps reedit those lines, and perform csh-like history expansion on previous commands. | |
libsodium | Sodium is a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. | |
libsvm | 3.24-IGB-gcc-4.9.4 | LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. |
LibTIFF | tiff: Library and tools for reading and writing TIFF data files | |
libtool | GNU libtool is a generic library support script. Libtool hides the complexity of using shared libraries behind a consistent, portable interface. - Homepage: http://www.gnu.org/software/libtool | |
libunwind | 1.3.1-IGB-gcc-4.9.4 1.5.0-IGB-gcc-8.2.0 |
The primary goal of libunwind is to define a portable and efficient C programming interface (API) to determine the call-chain of a program. The API additionally provides the means to manipulate the preserved (callee-saved) state of each call-frame and to resume execution at any point in the call-chain (non-local goto). The API supports both local (same-process) and remote (across-process) operation. As such, the API is useful in a number of applications |
LibUUID | Portable uuid C library | |
libXft | X11 client-side library | |
libxml2 | Libxml2 is the XML C parser and toolchain developed for the Gnome project (but usable outside of the Gnome platform). | |
libxslt | Libxslt is the XSLT C library developed for the GNOME project (but usable outside of the Gnome platform). | |
Liftoff | 1.6.1-IGB-gcc-8.2.0-Python-3.7.2 | Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. |
lima | 2.6.0-0 | lima is the standard tool to identify barcode and primer sequences in PacBio single-molecule sequencing data. It powers the Demultiplex Barcodes, Iso-Seq, and Mark PCR Duplicates GUI-based analysis applications. |
LINKS | 1.8.5-IGB-gcc-4.9.4-Perl-5.24.1 1.8.7-IGB-gcc-4.9.4-Perl-5.24.1 1.8.7-IGB-gcc-8.2.0-Perl-5.28.1 |
LINKS is a scalable genomics application for scaffolding or re-scaffolding genome assembly drafts with long reads, such as those produced by Oxford Nanopore Technologies Ltd and Pacific Biosciences. It provides a generic alignment-free framework for scaffolding and can work on any sequences. It is versatile and supports not only long sequences as a source of long-range information, but also MPET pairs and linked-reads, such as those from the 10X Genomics GemCode and Chromium platform, via ARCS (http://www.bcgsc.ca/platform/bioinfo/software/arcs). Fill gaps in LINKS-derived scaffolds using Sealer (http://www.bcgsc.ca/platform/bioinfo/software/sealer). |
LittleCMS | Little CMS intends to be an OPEN SOURCE small-footprint color management engine, with special focus on accuracy and performance. - Homepage: http://www.littlecms.com/ | |
LLVM | 10.0.1-IGB-gcc-8.2.0 4.0.1-IGB-gcc-4.9.4 6.0.0-IGB-gcc-4.9.4 |
The LLVM Core libraries provide a modern source- and target-independent optimizer, along with code generation support for many popular CPUs (as well as some less common ones!) These libraries are built around a well specified code representation known as the LLVM intermediate representation ("LLVM IR"). The LLVM Core libraries are well documented, and it is particularly easy to invent your own language (or port an existing compiler) to use LLVM as an optimizer and code generator. |
LMDB | 0.9.22-IGB-gcc-4.9.4 | LMDB is a fast, memory-efficient database. With memory-mapped files, it has the read performance of a pure in-memory database while retaining the persistence of standard disk-based databases. |
lofreq | 2.1.5-IGB-gcc-4.9.4-Python-2.7.13 | :oFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. |
longranger | 2.1.3 2.1.6 2.2.2 |
Long Ranger is a set of analysis pipelines that processes Chromium sequencing output to align reads and call and phase SNPs, indels, and structural variants. There are five main pipelines, each triggered by a longranger command |
lorals | 20210528-IGB-gcc-8.2.0-Python-3.7.2 | A Python package for allele-specific analyses in long-read data. |
Loter | 20210413-IGB-gcc-8.2.0-Python-3.7.2 | Loter is a Python (and soon R) package for local ancestry inference [1] and haplotype phasing [2] |
loupe | 2.1.1 | Loupe is a genome browser designed to visualize the Linked-Read data produced by the 10x Chromium Platform. Loupe is named for a jewelers loupe, which is used to inspect gems. |
lpsolve | 5.5.2.5-IGB-gcc-4.9.4 5.5.2.5-IGB-gcc-8.2.0 |
Mixed Integer Linear Programming (MILP) solver |
LRBinner | 0.1-IGB-gcc-8.2.0-Python-3.7.2 | Binning Error-Prone Long Reads Using Auto Encoders |
LTRretriever | 2.9.0-IGB-gcc-4.9.4-Perl-5.24.1 | LTR_retriever is a command line program (in Perl) for accurate identification of LTR retrotransposons (LTR-RTs) from outputs of LTRharvest, LTR_FINDER, MGEScan 3.0.0, LTR_STRUC, and LtrDetector, and generates non-redundant LTR-RT library for genome annotations. |
Lua | 5.1.5-IGB-gcc-4.9.4 5.1.5-IGB-gcc-8.2.0 5.3.4-IGB-gcc-4.9.4 |
Lua is a powerful, fast, lightweight, embeddable scripting language. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode for a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. |
lumpy-sv | 0.3.0-IGB-gcc-4.9.4 | A probabilistic framework for structural variant discovery. |
lz4 | 1.9.2-IGB-gcc-4.9.4 1.9.2-IGB-gcc-8.2.0 |
LZ4 is lossless compression algorithm, providing compression speed at 400 MB/s per core. It features an extremely fast decoder, with speed in multiple GB/s per core. |
lzo | Portable lossless data compression library | |
M4 | GNU M4 is an implementation of the traditional Unix macro processor. It is mostly SVR4 compatible although it has some extensions (for example, handling more than 9 positional parameters to macros). GNU M4 also has built-in functions for including files, running shell commands, doing arithmetic, etc. - Homepage: http://www.gnu.org/software/m4/m4.html | |
MACS | 1.4.2-1-IGB-gcc-4.9.4-Python-2.7.13 | Model-based Analysis of ChIP-Seq (MACS) on short reads sequencers such as Genome Analyzer (Illumina / Solexa). MACS empirically models the length of the sequenced ChIP fragments, which tends to be shorter than sonication or library construction size estimates, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome sequence, allowing for more sensitive and robust prediction. |
MACS2 | 2.1.1.20160309-IGB-gcc-4.9.4-Python-2.7.13 2.1.2-IGB-gcc-4.9.4-Python-2.7.13 2.2.5-IGB-gcc-4.9.4-Python-3.6.1 2.2.5-IGB-gcc-8.2.0-Python-3.7.2 |
Model Based Analysis for ChIP-Seq data |
MAFFT | 7.310-IGB-gcc-4.9.4 7.490-IGB-gcc-8.2.0 |
MAFFT is a multiple sequence alignment program for unix-like operating systems. It offers a range of multiple alignment methods, L-INS-i (accurate; for alignment of <∼200 sequences), FFT-NS-2 (fast; for alignment of <∼10,000 sequences), etc. |
mageck-vispr | 0.5.4-IGB-gcc-4.9.4-Python-3.6.1 | MAGeCK-VISPR is a comprehensive quality control, analysis and visualization workflow for CRISPR/Cas9 screens. |
magic | 0.1-IGB-gcc-4.9.4-Python-3.6.1 0.1.1-IGB-gcc-4.9.4-Python-3.6.1 |
A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data |
MAKER | 2.31.9-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 3.00.0-beta-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 3.01.03-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 3.01.1-beta-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded |
MAKER is a portable and easily configurable genome annotation pipeline. Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER is also easily trainable: outputs of preliminary runs can be used to automatically retrain its gene prediction algorithm, producing higher quality gene-models on seusequent runs. MAKERs inputs are minimal and its ouputs can be directly loaded into a GMOD database. They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER should prove especially useful for emerging model organism projects with minimal bioinformatics expertise and computer resources. |
Mako | 1.0.6-IGB-gcc-4.9.4-Python-3.6.1 | A super-fast templating language that borrows the best ideas from the existing templating languages |
manta | 1.6.0-IGB-gcc-8.2.0 | Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. |
mapDamage | 2.0.5-IGB-gcc-4.9.4-Python-2.7.13 2.0.9-IGB-gcc-4.9.4-Python-2.7.13 |
mapDamage2 is a computational framework written in Python and R, which tracks and quantifies DNA damage patterns among ancient DNA sequencing reads generated by Next-Generation Sequencing platforms. |
MAPGD | 0.4.26-IGB-gcc-4.9.4 | MAPGD is a series of related programs that estimate allele frequency, heterozygosity, Hardy-Weinberg disequilibrium, linkage disequilibrium and identity-by-descent (IBD) coefficients from population genomic data using a statistically rigorous maximum likelihood approach. |
MapSplice | 2.2.1-IGB-gcc-4.9.4-Python-2.7.13 | MapSplice is a software for mapping RNA-seq data to reference genome for splice junction discovery that depends only on reference genome, and not on any further annotations. |
MariaDB | 10.1.31-IGB-gcc-4.9.4 10.1.31-IGB-gcc-8.2.0 10.3.17-IGB-gcc-8.2.0 5.5.58-IGB-gcc-4.9.4 |
MariaDB An enhanced, drop-in replacement for MySQL. |
Mash | 2.2.2-IGB-gcc-8.2.0 | Fast genome and metagenome distance estimation using MinHash |
MashMap | 2.0 | MashMap implements a fast and approximate algorithm for computing local alignment boundaries between long DNA sequences. It can be useful for mapping genome assembly or long reads (PacBio/ONT) to reference genome(s). |
MaskRCNN | 20190227-IGB-gcc-4.9.4-Python-3.6.1 | This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. |
MaSuRCA | 3.2.3-IGB-gcc-4.9.4 3.4.2-IGB-gcc-8.2.0 4.0.5-IGB-gcc-8.2.0 |
MaSuRCA is whole genome assembly software. It combines the efficiency of the de Bruijn graph and Overlap-Layout-Consensus (OLC) approaches. MaSuRCA can assemble data sets containing only short reads from Illumina sequencing or a mixture of short reads and long reads (Sanger, 454, Pacbio and Nanopore). |
MATLAB | 2017a 2017b 2020b-IGB-gcc-8.2.0 |
MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran. |
MATLAB-python | 2017b-IGB-gcc-4.9.4-Python-3.6.1 2020b-IGB-gcc-8.2.0-Python-3.7.2 |
The MATLAB Engine API for Python provides a package for Python to call MATLAB as a computational engine. The engine supports the reference implementation (CPython) for Python versions 2.7, 3.5, and 3.6. |
mawk | 1.3.4-20200120-IGB-gcc-8.2.0 | mawk is an interpreter for the AWK Programming Language. |
maxbin2 | 2.2.7-IGB-gcc-4.9.4 | MaxBin2 is the next-generation of MaxBin (https://sourceforge.net/projects/maxbin/) that supports multiple samples at the same time. |
MaxQuant | 1.6.15.0-IGB-gcc-4.9.4 1.6.7.0-IGB-gcc-4.9.4 |
MaxQuant is a proteomics software package designed for analyzing large mass-spectrometric data sets. It is specifically aimed at high-resolution MS data. |
Mayo | 2024-IGB-gcc-8.2.0 | Mayo Class Module |
Mayo-Test | 2024-IGB-gcc-8.2.0 | Mayo Class Module |
McCortex | 1.0.1-IGB-gcc-4.9.4 | Multi-sample de novo assembly and variant calling using Linked de bruijn graphs. Variant calling with and without a reference genome. Between closely related samples or highly diverged ones. From bacterial to mammalian genomes. Minimal configuration. And it's free. |
MCL | 14.137-IGB-gcc-4.9.4 14.137-IGB-gcc-8.2.0 |
The MCL algorithm is short for the Markov Cluster Algorithm, a fastand scalable unsupervised cluster algorithm for graphs (also known as networks) basedon simulation of (stochastic) flow in graphs. |
MCScanX | 20221031-IGB-gcc-8.2.0 | |
medaka | 0.1.0-IGB-gcc-4.9.4-Python-3.6.1 | Medaka demonstrates a framework for error correcting sequencing data, particularly aimed at nanopore sequencing. Tools are provided for both training and inference. The code exploits the keras deep learning library. |
MEGAHIT | 1.1.1-IGB-gcc-4.9.4 1.2.9-IGB-gcc-8.2.0 |
MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph (SdBG) to achieve low memory assembly. |
megalodon | 1.0.0-IGB-gcc-4.9.4-Python-3.6.1 2.0.0-IGB-gcc-4.9.4-Python-3.6.1 2.1.1-IGB-gcc-8.2.0-Python-3.7.2 2.2.0-IGB-gcc-8.2.0-Python-3.7.2 2.2.4-IGB-gcc-8.2.0-Python-3.7.2 2.3.4-IGB-gcc-8.2.0-Python-3.7.2 |
Megalodon is a research tool for per-read and aggregated modified base and sequence variant calling by anchoring the information rich basecalling neural network output to a reference genome/transriptome. |
MEGAN | 6.12.2-Java-1.8.0_152 | MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application. |
MEME | 4.11.2-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 4.12.0-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 5.0.1-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 5.0.5-IGB-gcc-4.9.4 5.5.1-IGB-gcc-8.2.0 |
The MEME Suite allows you to: * discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences, * search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN, * compare a motif to all motifs in a database of motifs, * associate motifs with Gene Ontology terms via their putative target genes, and * analyse motif enrichment using SpaMo or CentriMo. |
memprof | 1.01 | Memprof is HPC Cluster Program Profiler. It records the memory, processor, and IO usage of a process. It will then generate a graph of the results. |
Meraculous-2d | 2.2.6-IGB-gcc-4.9.4 | Meraculous-2D is a whole genome assembler for NGS reads (Illumina) that is capable of assembling large, diploid genomes with modest computational requirements. |
Merfin | 1.1-IGB-gcc-8.2.0 20210507-IGB-gcc-8.2.0 |
k-mer-based assembly and variant calling evaluation for improved consensus accuracy. |
merlin-p | 20181020-IGB-gcc-8.2.0 | Modular regulatory network learning with per gene information (MERLIN) is a network inference method that tries to infer a more accurate regulatory network by incorporating a modularity constraint. |
merqury | 1.3-IGB-gcc-8.2.0 | Evaluate genome assemblies with k-mers and more |
meryl | 1.3 | |
Mesa | 20.0.2-IGB-gcc-4.9.4 | Mesa is an open-source implementation of the OpenGL specification - a system for rendering interactive 3D graphics. |
Meson | 0.51.2-IGB-gcc-4.9.4-Python-3.6.1 | Meson is a cross-platform build system designed to be both as fast and as user friendly as possible. |
MetaBAT | 2.12.1 2.15-IGB-gcc-8.2.0 |
MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities |
MetaEuk | 4-IGB-gcc-8.2.0 | MetaEuk is a modular toolkit designed for large-scale gene discovery and annotation in eukaryotic metagenomic contigs. MetaEuk combines the fast and sensitive homology search capabilities of MMseqs2 with a dynamic programming procedure to recover optimal exons sets. |
MetaGeneAnnotator | 20080819-x86-64 | MetaGeneAnnotator is a gene-finding program for prokaryote and phage. |
MetaGeneMark-2 | 20210406-IGB-gcc-8.2.0 | MetaGeneMark-2 is an unsupervised metagenomic gene finder. It improves on MetaGeneMark by adding models for better gene start prediction, as well as automatic selection of genetic code (4 or 11). |
metaMDBG | 0.3 | MetaMDBG is a fast and low-memory assembler for long and accurate metagenomics reads (e.g. PacBio HiFi). It is based on the minimizer de-Brujin graph (MDBG), which have been reimplemetend specifically for metagenomics assembly. |
metaphlan | 3.0.4-IGB-gcc-8.2.0-Python-3.7.2 3.0.7-IGB-gcc-8.2.0-Python-3.7.2 3.1.0-IGB-gcc-8.2.0-Python-3.7.2 4.0.0-IGB-gcc-8.2.0-Python-3.7.2 4.0.6-IGB-gcc-8.2.0-Python-3.7.2 |
MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. |
metaphlan2 | 2.6.0-IGB-gcc-4.9.4-Python-3.6.1 2.7.6-IGB-gcc-4.9.4-Python-2.7.13 2.7.8-IGB-gcc-4.9.4-Python-2.7.13 2.7.8-IGB-gcc-4.9.4-Python-3.6.1 |
MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea, Eukaryotes and Viruses) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. With the newly added StrainPhlAn module, it is now possible to perform accurate strain-level microbial profiling. |
metashape | 1.7.1-IGB-gcc-8.2.0 2.0.1 |
Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales. |
metashape-pro | 1.8.4-IGB-gcc-8.2.0 2.0.1 2.0.2 |
Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales. |
metashape-python | 1.7.1-IGB-gcc-8.2.0-Python-3.7.2 2.0.1-IGB-gcc-8.2.0-Python-3.7.2 |
Process digital images and generate 3D spatial data. Fast and highly accurate. |
MetaVelvet | 1.2.02-IGB-gcc-4.9.4 | An extension of Velvet assembler to de novo metagenome assembly from short sequence reads |
metaviralSPAdes | 20200721-IGB-gcc-8.2.0-Python-3.7.2 | It contains script for viral assembly from metagenomes (assembler/metaviralspades.py), which is based on metaplasmidSPAdes. |
metaWRAP | 1.2.3 1.3.2 |
MetaWRAP also includes a novel bin reassembly module, which allows to drastically improve the quality of a set of bins by extracting the reads belonging to each bin, and reassembling the bins with a more permissive, non-metagenomic assembler. |
METIS | 5.1.0-IGB-gcc-4.9.4 | METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes,and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on themultilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes. |
mfold | 4.7-IGB-gcc-8.2.0 | The mfold web server is one of the oldest web servers in computational molecular biology. |
microbiomeutil | 20110519-IGB-gcc-4.9.4 | Contains ChimeraSlayer, WigeoN, and NAST-iEr |
microbiome_helper | 20171114-IGB-gcc-4.9.4 | An assortment of scripts to help process and automate various microbiome and metagenomic bioinformatic tools. We provide workflows, tutorials and a virtual box image to help researchers process microbial data. |
miniasm | 0.2-IGB-gcc-4.9.4 | Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads. |
Miniconda2 | 4.7.12.1 | Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. |
Miniconda3 | 23.5.2 4.10.3 4.7.12.1 |
Built to complement the rich, open source Python community,the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. |
minimap | 0.2-IGB-gcc-4.9.4 2.11-IGB-gcc-4.9.4 2.16-IGB-gcc-4.9.4-Python-3.6.1 2.17-IGB-gcc-8.2.0 2.18-IGB-gcc-8.2.0 2.21-IGB-gcc-8.2.0 2.3-IGB-gcc-4.9.4 2.8-IGB-gcc-4.9.4 |
Minimap is an experimental tool to efficiently find multiple approximate mapping positions between two sets of long sequences, such as between reads and reference genomes, between genomes and between long noisy reads. |
minorseq | 20180314-IGB-gcc-4.9.4 | Minor Variant Calling and Phasing Tools |
mirdeep2 | 0.0.8-IGB-gcc-4.9.4 0.1.3-IGB-gcc-4.9.4 |
miRDeep2 is a completely overhauled tool which discovers microRNA genes by analyzing sequenced RNAs. |
mitofates | 1.2-IGB-gcc-4.9.4-Perl-5.24.1 | MitoFates predicts mitochondrial presequence, a cleavable localization signal located in N-terminal, and its cleaved position. |
mlst | 2.19.0-IGB-gcc-8.2.0-Perl-5.28.1 | Scan contig files against traditional PubMLST typing schemes |
mmquant | 1.0.4-IGB-gcc-8.2.0 | A tool to quantiy gene expression. The mmquant algorithm handles multiply mapping reads, i.e., duplicated genes by constructing merged genes. |
MMseqs2 | 10-6d92c | MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. |
modeltest-ng | 0.1.7 | ModelTest-NG is a tool for selecting the best-fit model of evolution for DNA and protein alignments. ModelTest-NG supersedes jModelTest and ProtTest in one single tool, with graphical and command console interfaces. |
Mono | An open source, cross-platform, implementation of C# and the CLR that is binary compatible with Microsoft.NET. | |
MOODS | 1.9.4.1-IGB-gcc-8.2.0-Python-3.7.2 | MOODS is a suite of algorithms for matching position weight matrices (PWM) against DNA sequences. It features advanced matrix matching algorithms implemented in C++ that can be used to scan hundreds of matrices against chromosome-sized sequences in few seconds. |
Mothur | 1.38.1.1 1.39.5 1.39.5-IGB-gcc-4.9.4 1.44.1-IGB-gcc-8.2.0 1.47.0-IGB-gcc-8.2.0 |
Mothur is a single piece of open-source, expandable software to fill the bioinformatics needs of the microbial ecology community. |
MPFR | The MPFR library is a C library for multiple-precision floating-point computations with correct rounding. | |
MPICH | 3.0.4-GCC-4.9.4-2.28 | MPICH v3.x is an open source high-performance MPI 3.0 implementation.It does not support InfiniBand (use MVAPICH2 with InfiniBand devices). |
msgpack | It's like JSON but smaller and faster. | |
msmc | 1.1.0 | This software implements MSMC, a method to infer population size and gene flow from multiple genome sequences |
mspminer | 2.0 | MSPminer reconstitutes Metagenomic Species Pan-genomes by binning co-abundant genes across metagenomic samples. |
MToolBox | 1.0-IGB-gcc-4.9.4 1.2.1-IGB-gcc-4.9.4 |
MToolBox is a highly automated bioinformatics pipeline to reconstruct and analyze human mitochondrial DNA from high throughput sequencing data. MToolBox includes an updated computational strategy to assemble mitochondrial genomes from Whole Exome and/or Genome Sequencing (PMID: 22669646) and an improved fragment-classify tool (PMID:22139932) for haplogroup assignment, functional and prioritization analysis of mitochondrial variants. MToolBox provides pathogenicity scores, profiles of genome variability and disease-associations for mitochondrial variants. MToolBox provides also a Variant Call Format file (version 4.0) featuring, for the first time, allele-specific heteroplasmy. |
multigeneblast | 1.1.12-IGB-gcc-4.9.4 | MultiGeneBlast is an open source tool for identification of homologs of multigene modules such as operons and gene clusters. It is based on a reformatting of the FASTA headers of NCBI GenBank protein entries, using which it can track down their source nucleotide and coordinates. |
MultiQC | 0.9-IGB-gcc-4.9.4-Python-2.7.13 1.11-IGB-gcc-8.2.0-Python-3.7.2 1.14-IGB-gcc-8.2.0-Python-3.7.2 1.15-IGB-gcc-8.2.0-Python-3.7.2 1.2-IGB-gcc-4.9.4-Python-2.7.13 1.6-IGB-gcc-4.9.4-Python-3.6.1 1.7-IGB-gcc-4.9.4-Python-3.6.1 1.7-IGB-gcc-8.2.0-Python-3.7.2 1.9-IGB-gcc-8.2.0-Python-3.7.2 |
MultiQC searches a given directory for analysis logs and compiles a HTML report. Its a general use tool, perfect for summarising the output from numerous bioinformatics tools. |
MUMmer | 3.23-IGB-gcc-4.9.4 3.23-IGB-gcc-8.2.0 4.0.0beta2-IGB-gcc-4.9.4 4.0.0beta2-IGB-gcc-8.2.0 4.0.0rc1-IGB-gcc-8.2.0 |
MUMmer is a system for rapidly aligning entire genomes |
MUSCLE | 3.8.31-IGB-gcc-4.9.4 | MUSCLE is one of the best-performing multiple alignment programs according to published benchmark tests, with accuracy and speed that are consistently better than CLUSTALW. MUSCLE can align hundreds of sequences in seconds. Most users learn everything they need to know about MUSCLE in a few minutes—only a handful of command-line options are needed to perform common alignment tasks. - Homepage: http://drive5.com/muscle/ |
NAMD | 20200428 | is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. |
nanopack | 1.0.0-IGB-gcc-4.9.4-Python-3.6.1 | Easily install all my long read processing and analysis scripts simultaneously. |
NanoPlot | 1.18.2-IGB-gcc-4.9.4-Python-3.6.1 | Plotting tool for long read sequencing data and alignments. |
nanopolish | 0.10.1-IGB-gcc-4.9.4 0.10.2-IGB-gcc-4.9.4 0.11.0-IGB-gcc-4.9.4 0.13.2-IGB-gcc-8.2.0 0.6.0-IGB-gcc-4.9.4 0.7.1-IGB-gcc-4.9.4 0.7.1-IGB-gcc-4.9.4-159d92b 0.8.3-IGB-gcc-4.9.4 0.8.5-IGB-gcc-4.9.4 0.9.0-IGB-gcc-4.9.4 |
Software package for signal-level analysis of Oxford Nanopore sequencing data. |
NanoSV | 1.2.4-IGB-gcc-8.2.0-Python-3.7.2 | NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers. |
NASM | NASM: General-purpose x86 assembler | |
ncbi-datasets | 20220607 20221101 20240305 |
Install and use the NCBI Datasets command line tools |
ncbi-genome-download | 0.2.9-IGB-gcc-4.9.4-Python-3.6.1 | Some script to download bacterial and fungal genomes from NCBI after they restructured their FTP a while ago. |
ncbi-toolkit | 22-IGB-gcc-8.2.0 | The NCBI Toolkit is a collection of utilities developed for the production and distribution of GenBank, Entrez, BLAST, and related services by the National Center for Biotechnology Information. |
ncbi-vdb | 2.11.0-IGB-gcc-8.2.0 2.8.2-IGB-gcc-4.9.4 |
The SRA Toolkit and SDK from NCBI is a collection of tools and libraries for using data in the INSDC Sequence Read Archives. |
ncdf4 | 1.16-IGB-gcc-4.9.4-R-3.3.3 | This package provides a high-level R interface to data files written using Unidatas netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. |
NCO | 4.7.2-IGB-gcc-4.9.4 5.0.1-IGB-gcc-8.2.0 |
manipulates and analyzes data stored in netCDF-accessible formats, including DAP, HDF4, and HDF5 |
ncurses | The Ncurses (new curses) library is a free software emulation of curses in System V Release 4.0, and more. It uses Terminfo format, supports pads and color and multiple highlights and forms characters and function-key mapping, and has all the other SYSV-curses enhancements over BSD Curses. | |
ncview | 2.1.7-IGB-gcc-8.2.0 | Ncview is a visual browser for netCDF format files.Typically you would use ncview to get a quick and easy, push-buttonlook at your netCDF files. You can view simple movies of the data,view along various dimensions, take a look at the actual data values,change color maps, invert the data, etc. |
netCDF | 4.4.1.1-IGB-gcc-4.9.4 4.7.2-IGB-gcc-8.2.0 |
NetCDF (network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. |
netCDF-Fortran | 4.4.1-IGB-gcc-4.9.4 4.5.2-IGB-gcc-8.2.0 |
NetCDF (network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. |
NetLogo | 5.2.1 6.0.3 |
NetLogo is a multi-agent programmable modeling environment. It is used by many tens of thousands of students, teachers and researchers worldwide. |
nettle | 3.3-IGB-gcc-4.9.4 | Nettle is a cryptographic library that is designed to fit easily in more or less any context: In crypto toolkits for object-oriented languages (C++, Python, Pike, ...), in applications like LSH or GNUPG, or even in kernel space. |
nextflow | 0.25.7-Java-1.8.0_121 0.26.3-Java-1.8.0_152 18.10.1-Java-1.8.0_152 19.07.0-Java-1.8.0_152 20.01.0-Java-1.8.0_152 21.03.0-Java-1.8.0_152 21.03.0-Java-1.8.0_201 21.04.1-Java-1.8.0_152 21.06.0-edge-Java-1.8.0_152 22.09.7-Java-11.0.5 22.10.1-Java-15.0.1 22.10.6-Java-15.0.1 23.10.0-Java-15.0.1 |
Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. |
NextPolish | 1.4.0-IGB-gcc-8.2.0 | NextPolish is used to fix base errors (SNV/Indel) in the genome generated by noisy long reads, it can be used with short read data only or long read data only or a combination of both. |
nf-core | 1.6-IGB-gcc-4.9.4-Python-3.6.1 2.7.2-IGB-gcc-8.2.0-Python-3.10.1 |
A community effort to collect a curated set of analysis pipelines built using Nextflow. |
NGS | NGS is a new, domain-specific API for accessing reads, alignments and pileupsproduced from Next Generation Sequencing. | |
NGSCheckMate | 20190507-IGB-gcc-8.2.0-Python-2.7.18 | NGSCheckMate is a software package for identifying next generation sequencing (NGS) data files from the same individual. It analyzes various types of NGS data files including (but not limited to) whole genome sequencing (WGS), whole exome sequencing (WES), RNA-seq, ChIP-seq, and targeted sequencing of various depths. |
ngsF | 1.2.0-IGB-gcc-8.2.0 | ngsF is a program to estimate per-individual inbreeding coefficients under a probabilistic framework that takes the uncertainty of genotype's assignation into account. It avoids calling genotypes by using genotype likelihoods or posterior probabilities. |
ngsF-HMM | 20200722-IGB-gcc-8.2.0 | ngsF-HMM is a program to estimate per-individual inbreeding tracts using a two-state Hidden Markov Model (HMM). |
ngsLD | 1.2.0-IGB-gcc-8.2.0 | ngsLD is a program to estimate pairwise linkage disequilibrium (LD) taking the uncertainty of genotype's assignation into account. It does so by avoiding genotype calling and using genotype likelihoods or posterior probabilities. |
ninja | 1.8.2-IGB-gcc-4.9.4 | Ninja is a small build system with a focus on speed. It differs from other build systems in two major respects: it is designed to have its input files generated by a higher-level build system, and it is designed to run builds as fast as possible. |
NINJA | 0.97-IGB-gcc-4.9.4 | Nearly Infinite Neighbor Joining Application |
Ninja | 1.9.0-IGB-gcc-4.9.4 | Ninja is a small build system with a focus on speed. |
nltk | 3.5-IGB-gcc-4.9.4-Python-3.6.1 3.5-IGB-gcc-8.2.0-Python-3.7.2 |
NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. |
nodejs | 10.16.2-IGB-gcc-4.9.4 14.15.0-IGB-gcc-8.2.0 9.9.0-IGB-gcc-4.9.4 |
Node.js is a platform built on Chromes JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices. |
nonpareil | 3.3.4-IGB-gcc-8.2.0 | Estimate average coverage and create Nonpareil curves for metagenomic datasets. |
nose-parameterized | 0.6.0-IGB-gcc-4.9.4-Python-2.7.13 | Parameterized testing with any Python test framework. |
novocraft | 3.08.00 | Powerful tool designed for mapping of short reads onto a reference genome from Illumina, Ion Torrent, and 454 NGS platforms. |
npm | v7.20.0-IGB-gcc-4.9.4 | the package manager for JavaScript |
NucleoATAC | 0.3.4-IGB-gcc-4.9.4-Python-2.7.13 | NucleoATAC is a python package for calling nucleosome positions and occupancy using ATAC-Seq data. Functions for calling nucleosomes are included in the nucleoatac command-line function. NucleoATAC also includes other utlities for working with ATAC-seq data under the pyatac function. |
numactl | The numactl program allows you to run your application program on specific cpus and memory nodes.It does this by supplying a NUMA memory policy to the operating system before running your program.The libnuma library provides convenient ways for you to add NUMA memory policies into your own program. | |
numba | 0.34.0-IGB-gcc-4.9.4-Python-2.7.13 0.34.0-IGB-gcc-4.9.4-Python-3.6.1 0.35.0-IGB-gcc-4.9.4-Python-2.7.13 0.52.0-IGB-gcc-8.2.0-Python-3.7.2 0.55.2-IGB-gcc-8.2.0-Python-3.7.2 0.59.0-IGB-gcc-8.2.0-Python-3.10.1 |
Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. |
numpy | The fundamental package for scientific computing with Python | |
NWChem | 6.6.revision27746-IGB-gcc-4.9.4-Python-2.7.13 | NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters. NWChem software can handle: biomolecules, nanostructures, and solid-state; from quantum to classical, and all combinations; Gaussian basis functions or plane-waves; scaling from one to thousands of processors; properties and relativity. |
OBITools3 | 3.0.0b42-IGB-gcc-8.2.0-Python-3.7.2 | The OBITools3: A package for the management of analyses and data in DNA metabarcoding |
OCaml | 4.05.0-IGB-gcc-4.9.4 | OCaml is a general purpose industrial-strength programming language with an emphasis on expressiveness and safety. Developed for more than 20 years at Inria it benefits from one of the most advanced type systems and supports functional, imperative and object-oriented styles of programming. |
octopus | 0.6.3-beta-IGB-gcc-8.2.0 | Octopus is a mapping-based variant caller that implements several calling models within a unified haplotype-aware framework. Octopus takes inspiration from particle filtering by constructing a tree of haplotypes and dynamically pruning and extending the tree based on haplotype posterior probabilities in a sequential manner. |
OligoMiner | 20181123-IGB-gcc-4.9.4 | A rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes |
Omni-C | 20210526-IGB-gcc-8.2.0-Python-3.7.2 | The Dovetail™ Omni-C™ library uses a sequence-independent endonuclease for chromatin digestion prior to proximity ligation and library generation. |
ont-fast5-api | 3.1.5-IGB-gcc-8.2.0-Python-3.7.2 3.3.0-IGB-gcc-8.2.0-Python-3.7.2 |
ont_fast5_api is a simple interface to HDF5 files of the Oxford Nanopore .fast5 file format. |
OpenBabel | 3.0.0-IGB-gcc-8.2.0 | Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It's an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas. |
OpenBLAS | OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version. | |
OpenCV | 3.1.0-IGB-gcc-4.9.4-Python-2.7.13 3.3.0-IGB-gcc-4.9.4-Python-3.6.1 4.5.2-IGB-gcc-8.2.0-Python-3.7.2 |
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. |
OpenMPI | 2.1.0-GCC-4.9.4-2.28 4.0.0-GCC-8.2.0-2.32 |
The Open MPI Project is an open source MPI-3 implementation. |
OpenPGM | OpenPGM is an open source implementation of the Pragmatic General Multicast (PGM) specification in RFC 3208 available at www.ietf.org. PGM is a reliable and scalable multicast protocol that enables receivers to detect loss, request retransmission of lost data, or notify an application of unrecoverable loss. PGM is a receiver-reliable protocol, which means the receiver is responsible for ensuring all data is received, absolving the sender of reception responsibility. | |
OpenSfM | 0.5.1-IGB-gcc-8.2.0-Python-3.7.2 | OpenSfM is a Structure from Motion library written in Python. The library serves as a processing pipeline for reconstructing camera poses and 3D scenes from multiple images. |
OpenSSL | 1.1.1m-IGB-gcc-8.2.0 | The OpenSSL Project is a collaborative effort to develop a robust, commercial-grade, full-featured, and Open Source toolchain implementing the Secure Sockets Layer (SSL v2/v3) and Transport Layer Security (TLS v1) protocols as well as a full-strength general purpose cryptography library. |
OrthoFinder | 2.2.7 2.3.7-IGB-gcc-4.9.4 2.5.4-IGB-gcc-8.2.0 |
OrthoFinder is a fast, accurate and comprehensive platform for comparative genomics. It finds orthologs and orthogroups, infers rooted gene trees for all orthogroups and identifies all of the gene duplcation events in those gene trees. |
OrthoMCL | 2.0.9-IGB-gcc-4.9.4-Perl-5.24.1 | OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. - Homepage: http://orthomcl.org/ |
OutPredict | 1.0.0-IGB-gcc-8.2.0-Python-3.7.2 | This repository contains OutPredict, a python developed Method for Predicting Out-of-sample Data in Time Series and Steady State data as well as to predict Causal connections from transcription factors to genes. |
p7zip | 17.04-IGB-gcc-8.2.0 | p7zip is a quick port of 7z.exe and 7za.exe (CLI version of7zip) for Unix. 7-Zip is a file archiver with highest compression ratio. |
Pacasus | 1.2-IGB-gcc-4.9.4-Python-2.7.13 | Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. |
PAGAN | 20150723-IGB-gcc-4.9.4 | PAGAN is a general-purpose method for the alignment of sequence graphs. PAGAN is based on the phylogeny-aware progressive alignment algorithm and uses graphs to describe the uncertainty in the presence of characters at certain sequence positions. However, graphs also allow describing the uncertainty in input sequences and modelling e.g. homopolymer errors in Roche 454 reads, or representing inferred ancestral sequences against which other sequences can then be aligned. PAGAN is still under development and will hopefully evolve to an easy-to-use, general-purpose method for phylogenetic sequence alignment. |
pagit | 1.0 | Tools to generate automatically high quality sequence by ordering contigs, closing gaps, correcting sequence errors and transferring annotation. |
pairix | 0.3.7-IGB-gcc-8.2.0 | Pairix is a tool for indexing and querying on a block-compressed text file containing pairs of genomic coordinates. |
pairtools | 0.3.0-IGB-gcc-4.9.4-Python-3.6.1 1.0.2-IGB-gcc-8.2.0-Python-3.7.2 |
pairtools is a simple and fast command-line framework to process sequencing data from a Hi-C experiment. |
pal2nal | 14-IGB-gcc-4.9.4 | PAL2NAL is a program that converts a multiple sequence alignment of proteins and the corresponding DNA (or mRNA) sequences into a codon alignment. |
PAML | 4.9e-IGB-gcc-4.9.4 | PAML is a package of programs for phylogenetic analyses of DNA or protein sequences using maximum likelihood. - Homepage: http://abacus.gene.ucl.ac.uk/software/paml.html |
PANDAseq | 2.11-IGB-gcc-4.9.4 | PANDAseq assembles Illumina Solexa overlapping pair-end reads. - Homepage: https://github.com/neufeld/pandaseq |
pandoc | 2.2.3.2 | If you need to convert files from one markup format into another, pandoc is your swiss-army knife |
Pango | Pango is a library for laying out and rendering of text, with an emphasis on internationalization.Pango can be used anywhere that text layout is needed, though most of the work on Pango so far has been done in thecontext of the GTK+ widget toolkit. Pango forms the core of text and font handling for GTK+-2.x. | |
pangolin | 2.3.2-IGB-gcc-8.2.0-Python-3.7.2 2.4.2-IGB-gcc-8.2.0-Python-3.7.2 |
Phylogenetic Assignment of Named Global Outbreak LINeages |
parabricks | 3.1.0-IGB-gcc-8.2.0-Python-3.7.2 3.7.0 |
NVIDIA Clara™ Parabricks is a computational framework supporting genomics applications from DNA to RNA. It employs NVIDIA’s CUDA, HPC, AI, and data analytics stacks to build GPU accelerated libraries, pipelines, and reference application workflows for primary, secondary, and tertiary analysis. |
parallel | 20170622-IGB-gcc-4.9.4 20200822-IGB-gcc-8.2.0 |
parallel: GNU parallel is a shell tool for executing jobs in parallel using one or more computers. |
pb-assembly | 0.0.6 0.0.8 |
PacBio Assembly Tool Suite |
pbaa | 1.0.3.0 | PacBio Amplicon Analysis (pbaa) separates complex mixtures of amplicon targets from genomic samples. The pbaa application is designed to cluster and generate high-quality consensus sequences from HiFi reads. |
pbbam | 1.0.7-IGB-gcc-8.2.0 | The pbbam software package provides components to create, query, & edit PacBio BAM files and associated indices. These components include a core C++ library, bindings for additional languages, and command-line utilities. |
pbccs | 4.0 6.4.0 |
ccs takes multiple subreads of the same SMRTbell molecule and combines them using a statistical model to produce one highly accurate consensus sequence, also called HiFi read, with base quality values. |
pbipa | 1.3.0 1.3.2 |
Improved Phased Assembler (IPA) is the official PacBio software for HiFi genome assembly. IPA was designed to utilize the accuracy of PacBio HiFi reads to produce high-quality phased genome assemblies. |
pbmm2 | 1.12.0 1.4.0 |
pbmm2 is a SMRT C++ wrapper for minimap2's C API. Its purpose is to support native PacBio in- and output, provide sets of recommended parameters, generate sorted output on-the-fly, and postprocess alignments. |
pbzip2 | 1.1.13-IGB-gcc-4.9.4 1.1.13-IGB-gcc-8.2.0 |
PBZIP2 is a parallel implementation of the bzip2 block-sorting file compressor that uses pthreads and achieves near-linear speedup on SMP machines. |
pcangsd | 0.9-IGB-gcc-4.9.4-Python-2.7.13 20220330-IGB-gcc-8.2.0-Python-3.7.2 |
Framework for analyzing low-depth next-generation sequencing (NGS) data in heterogeneous/structured populations using principal component analysis (PCA). |
PCRE | The PCRE library is a set of functions that implement regular expression pattern matching using the same syntax and semantics as Perl 5. | |
PCRE2 | The PCRE library is a set of functions that implement regular expression pattern matching using the same syntax and semantics as Perl 5. | |
PEAKachu | 0.1.0-IGB-gcc-8.2.0-Python-3.7.2 | Peak calling tool for CLIP-seq data |
PEAR | 0.9.8-IGB-gcc-4.9.4 | PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory. - Homepage: http://sco.h-its.org/exelixis/web/software/pear/ |
PEnG-motif | 1.0.1 | PEnG-motif is an open-source software package for searching statistically overrepresented motifs (position specific weight matrices, PWMs) in a set of DNA sequences. |
PePr | 1.1.21-IGB-gcc-4.9.4-Python-2.7.13 | PePr is a ChIP-Seq Peak-calling and Prioritization pipeline that uses a sliding window approach and models read counts across replicates and between groups with a negative binomial distribution. |
Peregrine | 1.5.4 | Peregrine & SHIMMER Genome Assembly Toolkit |
perfsuite | 1.1.4-IGB-gcc-4.9.4 | PerfSuite is a collection of tools, utilities, and libraries for software performance analysis where the primary design goals are ease of use, comprehensibility, interoperability, and simplicity. This software can provide a good "entry point" for more detailed performance analysis and can help point the way towards selecting other tools and/or techniques using more specialized software if necessary (for example, tools/libraries from academic research groups or third-party commercial software). |
Perl | 5.24.1-IGB-gcc-4.9.4 5.24.1-IGB-gcc-4.9.4-bare 5.26.1-IGB-gcc-4.9.4-unthreaded 5.28.1-IGB-gcc-8.2.0 |
Larry Wall's Practical Extraction and Report Language |
pfamscan | 1.6-IGB-gcc-4.9.4-Perl-5.24.1 | This readme should help you get started with "pfam_scan.pl", which is for usewith the HMMER3 version of HMMER. |
pfilt | 1.5-IGB-gcc-8.2.0 | The pfilt program is designed to mask out (i.e. replace amino acid characterswith Xs) regions of low complexity, coiled-coil regions and more regions withextremely biased amino acid compositions. |
pftoolsV3 | 3.2.12-IGB-gcc-8.2.0 | A suite of tools to build and search generalized profiles (protein and DNA). |
PGDSpider | 2.1.1.5-Java-1.8.0_201 | PGDSpider is a powerful automated data conversion tool for population genetic and genomics programs. It facilitates the data exchange possibilities between programs for a vast range of data types (e.g. DNA, RNA, NGS, microsatellite, SNP, RFLP, AFLP, multi-allelic data, allele frequency or genetic distances). |
pgmpy | 20180320-IGB-gcc-4.9.4-Python-3.6.1 | pgmpy is a python library for working with Probabilistic Graphical Models. |
Phantompeaktools | 1.2-IGB-gcc-4.9.4-R-3.4.1 | Custom SPP for Phantompeaktools |
Phantompeaktools-spp | Custom SPP for Phantompeaktools | |
PhiSpy | 2.3-IGB-gcc-4.9.4-Python-2.7.13 | A novel algorithm for finding prophages in microbial genomes that combines similarity-based and composition-based strategies |
phobius | 1.01 | A combined transmembrane topology and signal peptide predictor |
phrap | 1.090518-IGB-gcc-8.2.0 | A Finishing Package (BAM File Viewer, Assembly Editor, Autofinish, Autoreport, Autoedit, and Align Reads To Reference Sequence) |
phyloFlash | 3.3-IGB-gcc-4.9.4 | phyloFlash is a pipeline to rapidly reconstruct the SSU rRNAs and explore phylogenetic composition of an Illumina (meta)genomic or transcriptomic dataset. |
phylophlan | 3.0.1-IGB-gcc-8.2.0-Python-3.7.2 3.0.3-IGB-gcc-8.2.0-Python-3.7.2 |
PhyloPhlAn 3.0 is an integrated pipeline for large-scale phylogenetic profiling of genomes and metagenomes. |
phylophlan2 | 0.34-IGB-gcc-4.9.4-Python-3.6.1 | PhyloPhlAn is an integrated pipeline for large-scale phylogenetic profiling of genomes and metagenomes. |
PhyloSift | 1.0.0_01-IGB-gcc-4.9.4-Perl-5.24.1 | PhyloSift is a suite of software tools to conduct phylogeneticanalysis of genomes and metagenomes. |
picard | 1.77-Java-1.8.0_152 2.10.1-Java-1.8.0_152 2.27.5-Java-1.8.0_201 2.9.0-Java-1.8.0_121 2.9.4-Java-1.8.0_121 |
A set of tools (in Java) for working with next generation sequencing data in the BAM (http://samtools.github.io/hts-specs) format. |
PICRUSt | 1.1.1-IGB-gcc-4.9.4-Python-2.7.13 1.1.3-IGB-gcc-4.9.4-Python-2.7.13 |
PICRUSt (pronounced pie crust) is a bioinformatics software package designed to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. |
PICRUSt2 | 2.3.0-b-IGB-gcc-8.2.0-Python-3.7.2 2.4.1-IGB-gcc-8.2.0-Python-3.7.2 |
PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is a software for predicting functional abundances based only on marker gene sequences. |
pigz | 2.3.4-IGB-gcc-4.9.4 2.4-IGB-gcc-8.2.0 |
pigz, which stands for parallel implementation of gzip, is a fully functional replacement for gzip that exploits multiple processors and multiple cores to the hilt when compressing data. pigz was written by Mark Adler, and uses the zlib and pthread libraries. |
Pillow | 5.1.0-IGB-gcc-4.9.4-Python-2.7.13 5.1.0-IGB-gcc-4.9.4-Python-3.6.1 |
Pillow is the friendly PIL fork by Alex Clark and Contributors. PIL is the Python Imaging Library by Fredrik Lundh and Contributors. |
pilon | 1.22-Java-1.8.0_121 1.23-Java-1.8.0_152 |
Pilon is a software tool which can be used to:- Automatically improve draft assemblies- Find variation among strains, including large event detection |
piranha | 1.2.1-IGB-gcc-4.9.4 | iranha is a peak-caller for CLIP- and RIP-Seq data. It takes input in BED or BAM format and identifies regions of statistically significant read enrichment. Additional covariates may optionally be provided to further inform the peak-calling process. |
PITA | 6-IGB-gcc-4.9.4 | The PITA executable allows you to identify and score microRNA targets on UTRs. |
pixman | Pixman is a low-level software library for pixel manipulation, providing features such as imagecompositing and trapezoid rasterization. Important users of pixman are the cairo graphics library and the X server. | |
pkg-config | pkg-config is a helper tool used when compiling applications and libraries. It helps you insert the correct compiler options on the command line so an application can use gcc -o test test.c `pkg-config --libs --cflags glib-2.0` for instance, rather than hard-coding values on where to find glib (or other libraries). | |
plass | 2-c7e35-IGB-gcc-4.9.4 3-764a3 |
Plass (Protein-Level ASSembler) is a software to assemble short read sequencing data on a protein level. |
plastid | 0.5.1-IGB-gcc-8.2.0-Python-3.7.2 | plastid is a Python library for genomics and sequencing. It includes tools for exploratory data analysis (EDA) as well as a handful of scripts that implement common tasks. |
platanus | 1.2.4 | Platanus is a novel de novo sequence assembler that can reconstruct genomic sequences ofhighly heterozygous diploids from massively parallel shotgun sequencing data. |
Platypus | 20180622-IGB-gcc-4.9.4-Python-2.7.13 | The Platypus variant caller. |
plink | 1.07 1.90 |
This is a comprehensive update to Shaun Purcells PLINK command-line program, developed by Christopher Chang with support from the NIH-NIDDKs Laboratory of Biological Modeling, the Purcell Lab at Mount Sinai School of Medicine, and others. |
plotsr | 1.1.1-IGB-gcc-8.2.0-Python-3.10.1 | Plotsr generates high-quality visualisation of synteny and structural rearrangements between multiple genomes. For this, it uses the genomic structural annotations between multiple chromosome-level assemblies. |
pomoxis | 0.1.0-IGB-gcc-4.9.4-Python-3.6.1 | Pomoxis contains a set of services to perform analysis of squiggles as they are produced in real-time along with fast pipelines for generating draft assemblies. |
popins | 1.0.1-IGB-gcc-8.2.0 | Population-scale detection of novel-sequence insertions. |
popins2 | 20220127-IGB-gcc-8.2.0 | Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs |
popoolation2 | 1201-IGB-gcc-8.2.0-Perl-5.28.1 | PoPoolation2 allows to compare allele frequencies for SNPs between two or more populations and to identify significant differences. |
poppler | Poppler is a PDF rendering library based on the xpdf-3.0 code base. | |
popscle | 20210504-IGB-gcc-8.2.0 | popscle is a suite of population scale analysis tools for single-cell genomics data. The key software tools in this repository includes demuxlet (version 2) and freemuxlet, a genotyping-free method to deconvolute barcoded cells by their identities while detecting doublets. |
Porechop | 0.2.3-IGB-gcc-4.9.4-Python-3.6.1 | Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. |
poretools | 0.6.0-IGB-gcc-4.9.4-Python-2.7.13 | a toolkit for working with nanopore sequencing data from Oxford Nanopore. |
ppanini | 0.7.3.1-IGB-gcc-4.9.4-Python-2.7.13 | PPANINI (Prioritization and Prediction of functional Annotation for Novel and Important genes via automated data Network Integration) is a computational pipeline that ranks genes by employing a combination of community parameters such as prevalence and abundance across samples. |
pplacer | 1.1.alpha19 1.1.alpha19-IGB-gcc-8.2.0-Python-3.7.2 |
Pplacer places query sequences on a fixed reference phylogenetic tree to maximize phylogenetic likelihood or posterior probability according to a reference alignment. Pplacer is designed to be fast, to give useful information about uncertainty, and to offer advanced visualization and downstream analysis. |
PRANK | 170427-IGB-gcc-4.9.4 | PRANK is a probabilistic multiple alignment program for DNA, codon and amino-acid sequences. It’s based on a novel algorithm that treats insertions correctly and avoids over-estimation of the number of deletion events. In addition, PRANK borrows ideas from maximum likelihood methods used in phylogenetics and correctly takes into account the evolutionary distances between sequences. Lastly, PRANK allows for defining a potential structure for sequences to be aligned and then, simultaneously with the alignment, predicts the locations of structural units in the sequences. |
preseq | 2.0.3-IGB-gcc-4.9.4 3.1.2-IGB-gcc-8.2.0 |
The preseq package is aimed at predicting and estimating the complexity of a genomic sequencing library, equivalent to predicting and estimating the number of redundant reads from a given sequencing depth and how many will be expected from additional sequencing using an initial sequencing experiment. |
primer3 | 2.4.0-IGB-gcc-4.9.4 | Primer3 is a widely used program for designing PCR primers (PCR = "Polymerase Chain Reaction"). PCR is an essential and ubiquitous tool in genetics and molecular biology. Primer3 can also design hybridization probes and sequencing primers. |
prinseq | 0.20.4-IGB-gcc-4.9.4-Perl-5.24.1 | PRINSEQ can be used to filter, reformat, or trim your genomic and metagenomic sequence data. |
Prodigal | 2.6.3 | Fast, reliable protein-coding gene prediction for prokaryotic genomes. |
PROJ | Program proj is a standard Unix filter function which converts geographic longitude and latitude coordinates into cartesian coordinates | |
prokka | 1.13-IGB-gcc-4.9.4-Perl-5.24.1 1.14.6-IGB-gcc-4.9.4-Perl-5.24.1 1.14.6-IGB-gcc-8.2.0-Perl-5.28.1 |
Whole genome annotation is the process of identifying features of interest in a set of genomic DNA sequences, and labelling them with useful information. Prokka is a software tool to annotate bacterial, archaeal and viral genomes quickly and produce standards-compliant output files. |
Prost | 0.7.45-IGB-gcc-4.9.4-Python-2.7.13 | Prost (PRocessing Of Small Transcripts) is a python application that quantifies and annotates microRNA (miRNA) expression in metazoans with assembled genomes. Prost works by counting short transcripts within a user-specifiable length range. These counted transcripts are aligned to a user specifiable genome allowing for post-transcriptional modification (e.g. untemplated additions, editing, alternative cutting) and then "binned" together based on genomic location. Each bin is then annotated with databases of mature miRNAs, hairpins, and other types of RNAs (the databases may be derived from miRBase, Ensembls BioMart, other databases, or may be custom built by the user). |
proteinfer | 20220411-IGB-gcc-4.9.4-Python-3.6.1 | ProteInfer is an approach for predicting the functional properties of protein sequences using deep neural networks. |
ProtHint | 2.5.0-IGB-gcc-8.2.0 2.6.0-IGB-gcc-8.2.0 |
ProtHint is a pipeline for predicting and scoring hints (in the form of introns, start and stop codons) in the genome of interest by mapping and spliced aligning predicted genes to a database of reference protein sequences. |
protobuf | 2.6.1-IGB-gcc-4.9.4 23.4-IGB-gcc-8.2.0 3.5.0-IGB-gcc-4.9.4 |
Protocol Buffers (a.k.a., protobuf) are Googles language-neutral, platform-neutral, extensible mechanism for serializing structured data. |
protobuf-python | 3.19.4-IGB-gcc-8.2.0 | Python Protocol Buffers runtime library. |
prottest3 | 3.4.2 | ProtTest is a bioinformatic tool for the selection of best-fit models of aminoacid replacement for the data at hand. ProtTest makes this selection by finding the model in the candidate list with the smallest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) score or Decision Theory Criterion (DT). |
psipred | 4.0-IGB-gcc-8.2.0 4.02-IGB-gcc-8.2.0 |
The PSIPRED Workbench provides a range of protein structure prediction methods. The site can be used interactively via a web browser or programmatically via our REST API. For high-throughput analyses, downloads of all the algorithms are available. |
pssh | 2.3.1-IGB-gcc-4.9.4-Python-2.7.13 | PSSH provides parallel versions of OpenSSH and related tools, including pssh, pscp, prsync, pnuke and pslurp. This project includes psshlib which can be used within custom applications. |
pullseq | 1.0.2-IGB-gcc-4.9.4 | Utility program for extracting sequences from a fasta/fastq file |
purge_dups | 1.0.1-IGB-gcc-8.2.0-Python-3.7.2 1.2.5-IGB-gcc-8.2.0-Python-3.7.2 |
purge haplotigs and overlaps in an assembly based on read depth |
purge_haplotigs | 1.1.1-IGB-gcc-8.2.0 | A simple pipeline for reassigning primary contigs that should be labelled as haplotigs. |
py2cytoscape | 0.7.1-IGB-gcc-8.2.0-Python-3.7.2 | py2cytoscape is a collection of utilities to use Cytoscape and Cytoscape.js from Python. Network visualization feature is still limited in Python, but with this tool, you can access both of Cytoscape and Cytoscape.js as network visualization engines for your Python code! |
pyani | 0.2.10-IGB-gcc-4.9.4-Python-3.6.1 | Python module for average nucleotide identity analyses |
pybedtools | 0.7.10-IGB-gcc-4.9.4-Python-3.6.1 0.8.2-IGB-gcc-8.2.0-Python-3.7.2 0.9.0-IGB-gcc-8.2.0-Python-3.7.2 |
pybedtools is a Python package that wraps BEDTools, so you will need both installed. |
pybind11 | 2.9.2-IGB-gcc-8.2.0 | pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. |
pycairo | 1.19.1-IGB-gcc-8.2.0-Python-3.7.2 | Pycairo is a Python module providing bindings for the cairo graphics library. It depends on cairo >= 1.15.10 and works with Python 3.7+. Pycairo, including this documentation, is licensed under the LGPL-2.1-only OR MPL-1.1. |
PyCUDA | 2017.1-IGB-gcc-4.9.4-Python-2.7.13 | PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. - Homepage: https://mathema.tician.de/software/pycuda/ |
pyCUDA | 2024.1-IGB-gcc-8.2.0-Python-3.10.1 | PyCUDA lets you access Nvidia’s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist-so what’s so special about PyCUDA? |
pyfasta | 0.5.2-IGB-gcc-4.9.4-Python-2.7.13 | Stores a flattened version of the fasta file without spaces or headers and uses either a mmap of numpy binary format or fseek/fread so the sequence data is never read into memory. |
pyGenomeTracks | 3.8-IGB-gcc-8.2.0-Python-3.7.2 | pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. |
pygpu | 0.6.5-IGB-gcc-4.9.4-Python-2.7.13 | Python bindings for libgpuarray - Homepage: http://deeplearning.net/software/libgpuarray |
pygraphviz | 1.3.1-IGB-gcc-4.9.4-Python-3.6.1 | PyGraphviz is a Python interface to the Graphviz graph layout and visualization package. With PyGraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. |
pylearn2 | 20170525-IGB-gcc-4.9.4-Python-2.7.13 | Pylearn2 is a library designed to make machine learning research easy. - Homepage: https://github.com/lisa-lab/pylearn2 |
pymol | 2.3.4 | PyMOL is a user-sponsored molecular visualization system on an open-source foundation, maintained and distributed by Schrödinger. |
pypy | 7.3.5-IGB-gcc-4.9.4-Python-2.7.13 | A fast, compliant alternative implementation of Python |
PyRosetta | 2021.25-IGB-gcc-8.2.0-Python-3.7.2 | PyRosetta is an interactive Python-based interface to the powerful Rosetta molecular modeling suite. It enables users to design their own custom molecular modeling algorithms using Rosetta sampling methods and energy functions. |
Python | 2.7.13-IGB-gcc-4.9.4 2.7.18-IGB-gcc-8.2.0 3.10.1-IGB-gcc-8.2.0 3.6.1-IGB-gcc-4.9.4 3.7.2-IGB-gcc-8.2.0 |
Python is a programming language that lets you work more quickly and integrate your systems more effectively. |
PyTorch | 0.3.0-IGB-gcc-4.9.4-Python-3.6.1 0.4.0-IGB-gcc-4.9.4-Python-3.6.1 0.4.1-IGB-gcc-4.9.4-Python-2.7.13 1.0.1.post2-IGB-gcc-4.9.4-Python-3.6.1 1.11.0-IGB-gcc-8.2.0-Python-3.7.2 1.12.1-IGB-gcc-8.2.0-Python-3.10.1 1.12.1-IGB-gcc-8.2.0-Python-3.7.2 1.13.1-IGB-gcc-8.2.0-Python-3.10.1 1.13.1-IGB-gcc-8.2.0-Python-3.7.2 1.3.1-IGB-gcc-4.9.4-Python-3.6.1 1.4.0-IGB-gcc-4.9.4-Python-3.6.1 1.6.0-IGB-gcc-4.9.4-Python-3.6.1 1.7.0-IGB-gcc-8.2.0-Python-3.7.2 1.9.0-IGB-gcc-8.2.0-Python-3.7.2 |
Tensors and Dynamic neural networks in Pythonwith strong GPU acceleration. |
pytorch-geometric | 2.0.4-IGB-gcc-8.2.0-Python-3.7.2 | PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. |
PyZMQ | 16.0.2-IGB-gcc-4.9.4-Python-3.6.1 | Python bindings for ZeroMQ |
qgrs | 1.0-IGB-gcc-4.9.4 | C++ implementation of QGRS mapping algorithm - described here. This program differs from the actual algorithm used by QGRS Mapper recarding overlapping motifs and the maximum length of the motif. |
QIIME | 1.9.1 | QIIME is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. |
QIIME2 | 2017.10 2017.11 2017.12 2017.6 2017.8 2018.11 2018.2 2018.6 2018.8 2019.10 2019.4 2019.7 2020.2 2020.6 2020.8 2021.4 2022.8 2023.2 2023.7 |
QIIME is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. |
Qt | 4.8.7-IGB-gcc-4.9.4 4.8.7-IGB-gcc-8.2.0 |
Qt is a comprehensive cross-platform C++ application framework. |
qualimap | 2.2.1-Java-1.8.0_121 | Qualimap 2 is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts. |
quast | 4.6.1-IGB-gcc-4.9.4-Python-2.7.13 5.0.0-IGB-gcc-4.9.4-Python-3.6.1 5.0.2-IGB-gcc-8.2.0-Python-3.7.2 |
QUAST performs fast and convenient quality evaluation and comparison of genome assemblies. |
quickmerge | 0.2-IGB-gcc-4.9.4-Python-2.7.13 | quickmerge uses a simple concept to improve contiguity of genome assemblies based on long molecule sequences, often with dramatic outcomes. The program uses information from assemblies made with illumina short reads and PacBio long reads to improve contiguities of an assembly generated with PacBio long reads alone. This is counterintuitive because illumina short reads are not typically considered to cover genomic regions which PacBio long reads cannot. Although we have not evaluated this program for assemblies generated with Oxford nanopore sequences, the program should work with ONP-assemblies too. |
R | 2.15.3-IGB-gcc-4.9.4 3.1.2-IGB-gcc-4.9.4 3.2.5-IGB-gcc-4.9.4 3.3.3-IGB-gcc-4.9.4 3.4.1-IGB-gcc-4.9.4 3.4.2-IGB-gcc-4.9.4 3.5.0-IGB-gcc-4.9.4 3.6.0-IGB-gcc-8.2.0 4.0.3-IGB-gcc-8.2.0 4.1.2-IGB-gcc-8.2.0 4.2.2-IGB-gcc-8.2.0 4.3.2-IGB-gcc-8.2.0 4.4.0-IGB-gcc-8.2.0 |
R is a free software environment for statistical computing and graphics. |
racon | 0.5.0-IGB-gcc-4.9.4-Python-2.7.13 0.5.0-IGB-gcc-4.9.4-Python-3.6.1 1.4.13-IGB-gcc-8.2.0 |
Racon is intended as a standalone consensus module to correct raw contigs generated by rapid assembly methods which do not include a consensus step, such as Miniasm.The goal of Racon is to generate genomic consensus which is of similar or better quality compared to the output generated by assembly methods which employ both error correction and consensus steps, while providing a speedup of several times compared to those methods. |
radar | 1.3-IGB-gcc-8.2.0 | Welcome to radar |
radinitio | 1.1.0-IGB-gcc-4.9.4-Python-3.6.1 1.1.1-IGB-gcc-4.9.4-Python-3.6.1 |
RADinitio is a simulation pipeline for the assessment of RADseq experiments. Genetic data are forward simulated for a population of individuals from a designated reference genome. |
Ragout | 2.0-IGB-gcc-4.9.4 | Ragout (Reference-Assisted Genome Ordering UTility) is a tool for chromosome assembly using multiple references. |
RagTag | 1.0.2-IGB-gcc-8.2.0-Python-3.7.2 1.1.1-IGB-gcc-8.2.0-Python-3.7.2 |
RagTag, the successor to RaGOO, is a command line tool for reference-guided genome assembly improvement. |
RAILS | 1.5.1-IGB-gcc-8.2.0 | RAILS and Cobbler are genomics application for scaffolding and automated finishing of genome assemblies with long DNA sequences. They can be used to scaffold & finish high-quality draft genome assemblies with any long, preferably high-quality, sequences such as scaftigs/contigs from another genome draft. |
randfold | 2.0.1-IGB-gcc-4.9.4 | Minimum free energy of folding randomization test software |
RAxML | 8.2.12-IGB-gcc-4.9.4 | RAxML search algorithm for maximum likelihood based inference of phylogenetic trees. |
raxml-ng | 1.2.0 | RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion. Its search heuristic is based on iteratively performing a series of Subtree Pruning and Regrafting (SPR) moves, which allows to quickly navigate to the best-known ML tree. |
rclone | 1.41 1.52.3 1.60.0 |
Rclone is a command line program to sync files and directories to and from different cloud storage |
RcppGSL | 0.3.2-IGB-gcc-4.9.4-R-3.3.3 | Rcpp integration for GNU GSL vectors and matrices The GNU Scientific Library (or GSL) is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The RcppGSL package provides an easy-to-use interface between GSL data structures and R using concepts from Rcpp which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses Rcpp to connect to another third-party library. The autoconf script, inline plugin and example package can all be used as a stanza to write a similar package against another library. |
rdkit | 2020_03_3-IGB-gcc-4.9.4-Python-3.6.1 2021_03_1-IGB-gcc-8.2.0-Python-3.7.2 |
RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python. |
RDPClassifier | 2.12-Java-1.8.0_152 2.2-Java-1.8.0_152 |
The RDP Classifier is a naive Bayesian classifier which was developed to provide rapid taxonomic placement based on rRNA sequence data. The RDP Classifier can rapidly and accurately classify bacterial and archaeal 16s rRNA sequences, and Fungal LSU sequences. It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. |
READemption | 1.0.1-IGB-gcc-8.2.0-Python-3.7.2 | READemption is a pipeline for the computational evaluation of RNA-Seq data. It was originally developed to process dRNA-Seq reads (as introduced by Sharma et al., Nature, 2010 (Pubmed)) originating from bacterial samples. |
REAP | 1.2-IGB-gcc-8.2.0 | |
REAPR | 1.0.18-IGB-gcc-4.9.4 | REAPR is a tool that evaluates the accuracy of a genome assembly using mapped paired end reads, without the use of a reference genome for comparison. It can be used in any stage of an assembly pipeline to automatically break incorrect scaffolds and flag other errors in an assembly for manual inspection. It reports mis-assemblies and other warnings, and produces a new broken assembly based on the error calls. - Homepage: http://www.sanger.ac.uk/science/tools/reapr |
RECON | 1.08-IGB-gcc-4.9.4-Perl-5.24.1 | a package for automated de novo identification of repeat families from genomic sequences |
redbiom | 0.3.2-IGB-gcc-4.9.4-Python-3.6.1 | Redbiom is a cache service for sample metadata and sample data. |
RepeatMasker | 4.0.7-IGB-gcc-4.9.4-Perl-5.24.1 4.0.7-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded 4.1.1-IGB-gcc-4.9.4-Perl-5.24.1 4.1.2-p1-IGB-gcc-8.2.0-Perl-5.28.1 4.1.6-IGB-gcc-8.2.0-Perl-5.28.1 |
RepeatMasker is a program that screens DNA sequences for interspersed repeats and low complexity DNA sequences. |
RepeatModeler | 1.0.11-IGB-gcc-4.9.4-Perl-5.24.1 2.0.1-IGB-gcc-4.9.4-Perl-5.24.1 2.0.2a-IGB-gcc-4.9.4-Perl-5.24.1 |
RepeatModeler is a de novo transposable element (TE) family identification and modeling package. At the heart of RepeatModeler are three de-novo repeat finding programs ( RECON, RepeatScout and LtrHarvest/Ltr_retriever ) which employ complementary computational methods for identifying repeat element boundaries and family relationships from sequence data. |
RepeatScout | 1.0.5-IGB-gcc-4.9.4 | De Novo Repeat Finder, |
reveal | 0.2.2-IGB-gcc-4.9.4-Python-2.7.13 | REVEAL (REcursiVe Exact-matching ALigner) can be used to (multi) align whole genomes. |
RFMix | 20180503-IGB-gcc-4.9.4 | A discriminative method for local ancestry inference |
rgeos | 0.4-2-IGB-gcc-4.9.4-R-3.5.0 | nterface to Geometry Engine - Open Source (GEOS) using the C API for topology operations on geometries. |
RGT | 0.11.3-IGB-gcc-4.9.4-Python-2.7.13 | Regulatory Genomics Toolbox (RGT) is an open source python library for analysis of regulatory genomics. RGT is programmed in an oriented object fashion and its core classes provide functionality for handling regulatory genomics data. |
rMATS | 3.2.5-IGB-gcc-4.9.4-Python-2.7.13 | MATS is a computational tool to detect differential alternative splicing events from RNA-Seq data. The statistical model of MATS calculates the P-value and false discovery rate that the difference in the isoform ratio of a gene between two conditions exceeds a given user-defined threshold. From the RNA-Seq data, MATS can automatically detect and analyze alternative splicing events corresponding to all major types of alternative splicing patterns. MATS handles replicate RNA-Seq data from both paired and unpaired study design. |
rMATS-turbo | 4.1.1-IGB-gcc-8.2.0-Python-3.7.2 | rMATS turbo is the C/Cython version of rMATS (refer to http://rnaseq-mats.sourceforge.net). |
RMBlast | 2.11.0-IGB-gcc-8.2.0 2.14.1-IGB-gcc-8.2.0 2.6.0-IGB-gcc-4.9.4 2.9.0-IGB-gcc-4.9.4 |
RMBlast is a RepeatMasker compatible version of the standard NCBI blastn program. The primary difference between this distribution and the NCBI distribution is the addition of a new program "rmblastn" for use with RepeatMasker and RepeatModeler. |
Rmpi | 0.6-6-IGB-gcc-4.9.4-R-3.4.2 | An interface (wrapper) to MPI APIs. It also provides interactive R manager and worker environment. |
RNAhybrid | 2.1.2-IGB-gcc-4.9.4 | RNAhybrid is a tool for finding the minimum free energy hybridization of a long and a short RNA. The hybridization is performed in a kind of domain mode, ie. the short sequence is hybridized to the best fitting part of the long one. The tool is primarily meant as a means for microRNA target prediction. |
RNAmmer | 1.2-IGB-gcc-4.9.4-Perl-5.24.1 1.2-IGB-gcc-8.2.0-Perl-5.28.1 |
Ribosomal RNA sub units |
rnaquast | 2.1.0-IGB-gcc-8.2.0-Python-3.7.2 | rnaQUAST is a tool for evaluating RNA-Seq assemblies using reference genome and gene database. In addition, rnaQUAST is also capable of estimating gene database coverage by raw reads and de novo quality assessment using third-party software. |
rnaseqtools | 1.0.2-IGB-gcc-4.9.4 | This repo rnaseqtools provides a set of tools to process transcripts (mainly in gtf format). |
RNAstructure | 6.0-IGB-gcc-4.9.4 | RNAstructure is a complete package for RNA and DNA secondary structure prediction and analysis. It includes algorithms for secondary structure prediction, including facility to predict base pairing probabilities. |
roary | 3.13.0-IGB-gcc-4.9.4-Perl-5.24.1 | Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (produced by Prokka (Seemann, 2014)) and calculates the pan genome. |
rodeo2 | 2.4.2-IGB-gcc-8.2.0-Python-3.7.2 | RODEO evaluates one or many genes, characterizing a gene neighborhood based on the presence of profile hidden Markov models (pHMMs). |
root | 6.14.06-IGB-gcc-4.9.4 6.14.06-IGB-gcc-4.9.4-Python-2.7.13 |
A modular scientific software toolkit. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R. |
root-painter-trainer | 0.2.19.1-IGB-gcc-8.2.0-Python-3.10.1 | RootPainter is a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter uses a client-server architecture, allowing users with a typical laptop to utilise a GPU on a more computationally powerful server. |
ROSE | 20150210-IGB-gcc-4.9.4-Python-2.7.13 | To create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) |
rosetta | 2017.52-IGB-gcc-4.9.4 3.12-IGB-gcc-4.9.4 3.13-IGB-gcc-8.2.0 |
The Rosetta software suite includes algorithms for computational modeling and analysis of protein structures. |
rpy | 1.0.3-IGB-gcc-4.9.4-Python-2.7.13-R-2.15.3 | RPy is a very simple, yet robust, Python interface to the R Programming Language. Itcan manage all kinds of R objects and can execute arbitrary R functionsincluding the graphic functions. |
rpy2 | 2.8.6-IGB-gcc-4.9.4-Python-2.7.13-R-3.3.3 2.8.6-IGB-gcc-4.9.4-Python-2.7.13-R-3.5.0 2.9.0-IGB-gcc-4.9.4-Python-3.6.1-R-3.3.3 2.9.5-IGB-gcc-8.2.0-Python-3.7.2-R-4.1.2 3.0.1-IGB-gcc-4.9.4-Python-3.6.1-R-3.5.0 3.2.4-IGB-gcc-8.2.0-Python-3.7.2-R-3.6.0 3.4.5-IGB-gcc-8.2.0-Python-3.7.2-R-4.1.2 |
rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions. |
RREFinder | 1.0.2-IGB-gcc-8.2.0-Python-3.7.2 | Bioinformatic application for the detection of RREs in protein sequences of interest |
RSAT | 20230828-IGB-gcc-8.2.0 | We offer tools to analyse cis-regulatory elements in genome sequences, motif discovery (support genome-wide data sets like ChIP-seq), transcription factor binding motif analysis (quality assessment, comparisons and clustering), comparative genomics, analysis of regulatory variations |
RSEM | 1.3.0-IGB-gcc-4.9.4 1.3.1-IGB-gcc-4.9.4 1.3.3-IGB-gcc-8.2.0 |
RNA-Seq by Expectation-Maximization |
RSeQC | 2.6.4-IGB-gcc-4.9.4-Python-2.7.13 | RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. Some basic modules quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, transcript level RNA integrity etc. |
RStudio | 2023.09.1-494 | Used by millions of people weekly, the RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R and Python. |
rtg-tools | 3.8.4 | RTG Tools contains utilities to easily manipulate and accurately compare multiple VCF files, as well as utilities for processing other common NGS data formats. |
Ruby | 2.4.2-IGB-gcc-4.9.4 | Ruby is a dynamic, open source programming language with a focus on simplicity and productivity. It has an elegant syntax that is natural to read and easy to write. |
Rust | 1.41.1 | Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. |
sabre | 20171114-IGB-gcc-4.9.4 | Next-generation sequencing can currently produce hundreds of millions of reads per lane of sample and that number increases at a dizzying rate. Barcoding individual sequences for multiple lines or multiple species is a cost-efficient method to sequence and analyze a broad range of data. |
SAI-app | 20230425-IGB-gcc-8.2.0-Python-3.7.2 | StomaAI application |
Salmon | 0.11.3-IGB-gcc-4.9.4 0.12.0-IGB-gcc-8.2.0 0.13.1-IGB-gcc-8.2.0 0.14.1-IGB-gcc-8.2.0 0.8.2-IGB-gcc-4.9.4-Python-2.7.13 0.9.1-IGB-gcc-4.9.4 1.0.0-IGB-gcc-8.2.0 1.1.0-IGB-gcc-8.2.0 1.10.0-IGB-gcc-8.2.0 1.2.1-IGB-gcc-8.2.0 1.4.0-IGB-gcc-8.2.0 1.5.2-IGB-gcc-8.2.0 |
Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. |
SalmonTE | 0.4-IGB-gcc-8.2.0 | SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances from Next Generation Sequencing Data. |
SalmonTools | 20190604-IGB-gcc-8.2.0 | Useful tools for working with Salmon output |
SALSA | 2.2-IGB-gcc-4.9.4-Python-2.7.13 2.3-IGB-gcc-4.9.4-Python-2.7.13 |
A tool to scaffold long read assemblies with Hi-C |
salt | beta0.2-IGB-gcc-8.2.0 | alignment algorithm that is SNP-aware |
sambamba | 0.6.6 0.8.0 |
sambamba view allows to efficiently filter SAM/BAM/CRAM files for alignments satisfying various conditions, as well as access its SAM header and information about reference sequences. |
samblaster | 0.1.24-IGB-gcc-4.9.4 | ummary samblaster is a fast and flexible program for marking duplicates in read-id grouped1 paired-end SAM files. It can also optionally output discordant read pairs and/or split read mappings to separate SAM files, and/or unmapped/clipped reads to a separate FASTQ file. When marking duplicates, samblaster will require approximately 20MB of memory per 1M read pairs. |
SAMtools | 0.1.18-IGB-gcc-4.9.4 0.1.20-IGB-gcc-4.9.4 0.1.20-IGB-gcc-8.2.0 1.10-IGB-gcc-8.2.0 1.11-IGB-gcc-8.2.0 1.12-IGB-gcc-8.2.0 1.17-IGB-gcc-8.2.0 1.3.1-IGB-gcc-4.9.4 1.4-IGB-gcc-4.9.4 1.4.1-IGB-gcc-4.9.4 1.5-IGB-gcc-4.9.4 1.7-IGB-gcc-4.9.4 1.9-IGB-gcc-4.9.4 |
SAM Tools provide various utilities for manipulating alignments in the SAM format, including sorting, merging, indexing and generating alignments in a per-position format. |
sbanalyzer | 3.0-14 3.1-2 |
Shoreline Biome’s unique microbiome kits are the key to generating long-read next-generation sequencing data necessary to discriminate microbial strains from any sample. |
ScaLAPACK | The ScaLAPACK (or Scalable LAPACK) library includes a subset of LAPACK routines redesigned for distributed memory MIMD parallel computers. | |
scallop | 0.10.3-IGB-gcc-4.9.4 0.10.4-IGB-gcc-4.9.4 |
Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. |
scikit-cuda | 0.5.1-IGB-gcc-4.9.4-Python-2.7.13 | scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIAs CUDA Programming Toolkit, as well as interfaces to select functions in the free and standard versions of the CULA Dense Toolkit. Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy are provided. |
scoary | 1.6.16-IGB-gcc-4.9.4-Python-3.6.1 | Scoary is designed to take the gene_presence_absence.csv file from Roary as well as a traits file created by the user and calculate the assocations between all genes in the accessory genome and the traits. It reports a list of genes sorted by strength of association per trait. |
SCons | 3.0.1-IGB-gcc-4.9.4-Python-2.7.13 3.0.1-IGB-gcc-4.9.4-Python-3.6.1 |
SCons is a software construction tool. |
scrappie | 1.3.0-IGB-gcc-4.9.4 | Scrappie is a technology demonstrator for the Oxford Nanopore Research Algorithms group. |
SDL2 | 2.0.4-IGB-gcc-4.9.4 | SDL: Simple DirectMedia Layer, a cross-platform multimedia library |
segemehl | 0.3.4-IGB-gcc-8.2.0 | segemehl is a software to map short sequencer reads to reference genomes. segemehl implements a matching strategy based on enhanced suffix arrays (ESA) |
Segway | 2.0.1-IGB-gcc-4.9.4-Python-2.7.13 | Segway is a tool for easy pattern discovery and identification in functional genomics data. |
sentieon | 201808 201911 202010.01 202112 202112.01 202112.04 202112.06 202308 202308.02 |
A fast and accurate solution to variant calling from next-generation sequence data |
SEPP | 20180223-IGB-gcc-4.9.4-Python-2.7.13 4.3.10-IGB-gcc-8.2.0-Python-3.7.2 4.5.1-IGB-gcc-8.2.0-Python-3.7.2 |
SEPP stands for "SATe-enabled Phylogenetic Placement", and addresses the problem of phylogenetic placement of short reads into reference alignments and trees. |
seqan | 2.2.0-IGB-gcc-8.2.0 2.3.2-IGB-gcc-4.9.4 |
SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data |
seqkit | 0.12.1 0.15.0 2.0.0 2.3.0 2.5.1 2.6.1 |
a cross-platform and ultrafast toolkit for FASTA/Q file manipulation |
SeqLib | C++ interface to HTSlib, BWA-MEM and Fermi | |
SeqPrep | 1.3.2-IGB-gcc-8.2.0 | SeqPrep is a program to merge paired end Illumina reads that are overlapping into a single longer read. It may also just be used for its adapter trimming feature without doing any paired end overlap. |
seqtk | 1.2-IGB-gcc-4.9.4 1.2-IGB-gcc-8.2.0 1.3-IGB-gcc-8.2.0 |
Seqtk is a fast and lightweight tool for processing sequences in the FASTA or FASTQ format. It seamlessly parses both FASTA and FASTQ files which can also be optionally compressed by gzip. |
SequelTools | 20200619-IGB-gcc-8.2.0-Python-3.7.2 | SequelTools is a fast and easy to install command-line program that provides a collection of tools for working with multiple SMRTcells of BAM format PacBio Sequel raw sequece data. |
shapeit | 2.20 | SHAPEIT is a fast and accurate method for estimation of haplotypes (aka phasing) from genotype or sequencing data. |
sharutils | 4.15.2-IGB-gcc-4.9.4 | GNU shar makes so-called shell archives out of many files, preparing them for transmission by electronic mail services, while unshar helps unpacking shell archives after reception. |
shasta | 0.2.0 | The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells. |
shortbred | 0.9.5-IGB-gcc-4.9.4-Python-2.7.13 | ShortBRED is a pipeline to take a set of protein sequences, reduce them to a set of unique identifying strings ("markers"), and then search for these markers in metagenomic data and determine the presence and abundance of the protein families of interest. |
Sibelia | 3.0.7-IGB-gcc-4.9.4 | Sibelia is a tool for finding synteny blocks in closely related genomes, like different strains of the same bacterial species. |
sickle | 1.33-IGB-gcc-8.2.0 | A windowed adaptive trimming tool for FASTQ files using quality |
SignalP | 4.1 | Signal peptide and cleavage sites in gram+, gram- and eukaryotic amino acid sequences |
simba | 1.3.0 | The SimBA region of interest (ROI) interface allows users to define and draw ROIs on videos. ROI data can be used to calculate basic descriptive statistics based on animals movements and locations such as: |
singularity | 3.4.1 3.8.1 |
Application and environment virtualization |
skylign | 1.1-IGB-gcc-4.9.4-Perl-5.24.1 | Skylign is a tool for creating logos representing both sequence alignments and profile hidden Markov models. Submit to the form on the right in order to produce (i) interactive logos for inclusion in webpages, or (ii) static logos for use in documents. |
sleap | 1.2.4-IGB-gcc-8.2.0-Python-3.7.2 | SLEAP is an open source deep-learning based framework for multi-animal pose tracking. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading. |
SMART-Aptamer-v1 | 20200313-IGB-gcc-4.9.4 | |
SMART-Aptamer-v2 | 20200315-IGB-gcc-4.9.4 | |
SMARTdenovo | 20170916-IGB-gcc-4.9.4-Perl-5.24.1 | SMARTdenovo is a de novo assembler for PacBio and Oxford Nanopore (ONT) data. It produces an assembly from all-vs-all raw read alignments without an error correction stage. It also provides tools to generate accurate consensus sequences, though a platform dependent consensus polish tools (e.g. Quiver for PacBio or Nanopolish for ONT) are still required for higher accuracy. |
smrtlink | 10.0.0.108728 11.0.0.146107 11.1.0.166339 8.0.0.80529 9.0.0.92188 |
PacBio’s open-source SMRT Analysis software suite is designed for use with Single Molecule, Real-Time (SMRT) Sequencing data. You can analyze, visualize, and manage your data through an intuitive GUI or command-line interface. |
smudgeplot | 0.2.5-IGB-gcc-8.2.0-Python-3.7.2 | This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. |
snakemake | 6.0.5-IGB-gcc-8.2.0-Python-3.7.2 | The Snakemake workflow management system is a tool to create reproducible and scalable data analyses. Workflows are described via a human readable, Python based language. |
SNAP | 2013-11-29-IGB-gcc-4.9.4 | (Semi-HMM-based Nucleic Acid Parser) gene prediction tool |
snapgene-reader | 0.1.19-IGB-gcc-8.2.0-Python-3.7.2 | SnapGene Reader is a Python library to parse Snapgene *.dna files into dictionaries or Biopython SeqRecords |
Sniffles | 1.0.12b-IGB-gcc-8.2.0 | Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs (10bp+) using evidence from split-read alignments, high-mismatch regions, and coverage analysis. |
snoscan | 0.9.1-IGB-gcc-4.9.4-Perl-5.24.1 0.9.1-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded |
Search for C/D box methylation guide snoRNA genes in a genomic sequence |
snpEff | 4.3o-Java-1.8.0_121 4.3t-Java-1.8.0_152 5.0e-Java-15.0.1 5.1f-Java-15.0.1 |
SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes). |
snpomatic | 1.0-IGB-gcc-4.9.4 | SNP-o-matic is a fast, stringent short-read mapping software. It supports a multitude of output types and formats, for uses in filtering reads, alignments, sequence-based genotyping calls, assisted reassembly of contigs etc. |
SNPsplit | 0.6.0-IGB-gcc-8.2.0-Perl-5.28.1 | SNPsplit is an allele-specific alignment sorter which is designed to read alignment files in SAM/BAM format and determine the allelic origin of reads that cover known SNP positions. |
SNVer | 0.5.3-Java-1.8.0_121 0.5.3-Java-1.8.0_152 |
SNVer is a statistical tool for calling common and rare variants in analysis of pool or individual next-generation sequencing data. It reports one single overall p-value for evaluating the significance of a candidate locus being a variant, based on which multiplicity control can be obtained. |
SOAPdenovo-Trans | 1.03 | SOAPdenovo-Trans is a de novo transcriptome assembler basing on the SOAPdenovo framework, adapt to alternative splicing and different expression level among transcripts.The assembler provides a more accurate, complete and faster way to construct the full-length transcript sets. |
SOAPdenovo2 | r242-IGB-gcc-8.2.0 | SOAPdenovo is a novel short-read assembly method that can build a de novo draft assembly for the human-sized genomes. The program is specially designed to assemble Illumina GA short reads. It creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost effective way. |
songbird | 1.0.3-IGB-gcc-4.9.4-Python-3.6.1 | Establishing microbial composition measurement standards with reference frames |
sortmerna | 2.1 4.3.6 |
SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. |
sourcetracker | 2.0.1-IGB-gcc-4.9.4-Python-3.6.1 | Bayesian community-wide culture-independent microbial source tracking |
sourmash | 4.6.1-IGB-gcc-8.2.0-Python-3.10.1 | Quickly search, compare, and analyze genomic and metagenomic data sets. |
SoX | 14.4.2-IGB-gcc-4.9.4 | Sound eXchange, the Swiss Army knife of audio manipulation |
spaceranger | 1.0.0 1.1.0 1.2.2 1.3.0 2.0.0 2.1.0 |
Space Ranger is a set of analysis pipelines that process Visium spatial RNA-seq output and brightfield and fluorescence microscope images in order to detect tissue, align reads, generate feature-spot matrices, perform clustering and gene expression analysis, and place spots in spatial context on the slide image. |
spaCy | 2.3.2-IGB-gcc-4.9.4-Python-3.6.1 | spaCy is designed to help you do real work — to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. |
SPAdes | 3.10.1-IGB-gcc-4.9.4-Python-2.7.13 3.11.0-IGB-gcc-4.9.4-Python-2.7.13 3.11.0-IGB-gcc-4.9.4-Python-3.6.1 3.11.1-IGB-gcc-4.9.4-Python-3.6.1 3.13.1-IGB-gcc-8.2.0-Python-3.7.2 3.14.1-IGB-gcc-8.2.0-Python-3.7.2 3.15.0-IGB-gcc-8.2.0-Python-3.7.2 3.15.3-IGB-gcc-8.2.0-Python-3.7.2 3.15.5-IGB-gcc-8.2.0-Python-3.7.2 |
SPAdes . St. Petersburg genome assembler . is intended for both standard isolates and single-cell MDA bacteria assemblies. |
sparsehash | 2.0.3-IGB-gcc-4.9.4 2.0.3-IGB-gcc-8.2.0 |
This directory contains several hash-map implementations, similar inAPI to SGIs hash_map class, but with different performance characteristics. |
Spine | 0.2.1-IGB-gcc-4.9.4 | Spine identifies a core genome from input genomic sequences. Sequences are aligned using Nucmer and regions found to be in common between all or a user-defined subset of genomes will be returned. |
SQLite | 3.17.0-IGB-gcc-4.9.4 3.17.0-IGB-gcc-8.2.0 3.30.1-IGB-gcc-4.9.4 |
SQLite: SQL Database Engine in a C Library |
squid | 1.9g-IGB-gcc-4.9.4 | A C library that is bundled with much of the above software. C function library for sequence analysis. |
SRA-Toolkit | 2.10.5 2.10.9 2.8.2-1 3.0.0 |
The NCBI SRA Toolkit enables reading (dumping) of sequencing files from the SRA database and writing (loading) files into the .sra format |
ssaha2 | 2.5.5-IGB-gcc-4.9.4 | SSAHA2 (Sequence Search and Alignment by Hashing Algorithm) is a pairwise sequence alignment program designed for the efficient mapping of sequencing reads onto genomic reference sequences. |
ssaha_pileup | 0.6-IGB-gcc-4.9.4 | SSAHA2: Sequence Search and Alignment by Hashing Algorithm |
SSW | SSW is a fast implementation of the Smith-Waterman algorithm, which uses the Single-Instruction Multiple-Data (SIMD) instructions to parallelize the algorithm at the instruction level. SSW library provides an API that can be flexibly used by programs written in C, C++ and other languages. We also provide a software that can do protein and genome alignment directly. Current version of our implementation is ~50 times faster than an ordinary Smith-Waterman. It can return the Smith-Waterman score, alignment location and traceback path (cigar) of the optimal alignment accurately; and return the sub-optimal alignment score and location heuristically. | |
Stacks | 1.47-IGB-gcc-4.9.4 2.54-IGB-gcc-8.2.0 2.62-IGB-gcc-8.2.0 |
Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography. |
STAR | 2.5.3a-IGB-gcc-4.9.4 2.6.0c-IGB-gcc-4.9.4 2.6.1b-IGB-gcc-4.9.4 2.7.0d-IGB-gcc-8.2.0 2.7.0f-IGB-gcc-8.2.0 2.7.10a-IGB-gcc-8.2.0 2.7.3a-IGB-gcc-8.2.0 2.7.4a-IGB-gcc-8.2.0 2.7.6a-IGB-gcc-8.2.0 |
STAR aligns RNA-seq reads to a reference genome using uncompressed suffix arrays. |
STAR-Fusion | 1.6.0-IGB-gcc-4.9.4 | STAR-Fusion is a component of the Trinity Cancer Transcriptome Analysis Toolkit (CTAT). STAR-Fusion uses the STAR aligner to identify candidate fusion transcripts supported by Illumina reads. |
strauto | 1-IGB-gcc-4.9.4 | Automation and Parallelization of STRUCTURE Analysis |
strelka | 2.9.9 | Strelka2 is a fast and accurate small variant caller optimized for analysis of germline variation in small cohorts and somatic variation in tumor/normal sample pairs. |
StringTie | 1.3.3-IGB-gcc-4.9.4 1.3.6-IGB-gcc-4.9.4 |
StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. |
stringtie | 2.1.1-IGB-gcc-4.9.4 | Transcript assembly and quantification for RNA-Seq |
structure | 2.3.4-IGB-gcc-4.9.4 | The program structure is a free software package for using multi-locus genotype data to investigate population structure. |
Subread | 1.5.2-IGB-gcc-4.9.4 1.6.3-IGB-gcc-4.9.4 2.0.0-IGB-gcc-8.2.0 2.0.4-IGB-gcc-8.2.0 |
High performance read alignment, quantification and mutation discovery |
SuiteSparse | 5.4.0-IGB-gcc-4.9.4-METIS-5.1.0 5.8.1-IGB-gcc-8.2.0 |
SuiteSparse is a collection of libraries manipulate sparse matrices. |
SUNDIALS | 2.6.2-IGB-gcc-4.9.4 | SUNDIALS: SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers |
Supernova | 1.1.5 1.2.0 1.2.1 2.0.0 2.0.1 2.1.0 2.1.1 |
Supernova is a software package for de novo assembly from Chromium Linked-Reads that are made from a single whole-genome library from an individual DNA source. A key feature of Supernova is that it creates diploid assemblies, thus separately representing maternal and paternal chromosomes over very long distances. |
SURVIVOR | 1.0.7-IGB-gcc-8.2.0 | SURVIVOR is a tool set for simulating/evaluating SVs, merging and comparing SVs within and among samples, and includes various methods to reformat or summarize SVs. |
svtools | 0.5.1-IGB-gcc-4.9.4-Python-2.7.13 | Comprehensive utilities to explore structural variations in genomes |
svtyper | 0.7.1-IGB-gcc-4.9.4-Python-2.7.13 | SVTyper performs breakpoint genotyping of structural variants (SVs) using whole genome sequencing data. |
swift-t | 1.3-IGB-gcc-4.9.4 | Swift/T is a completely new implementation of the Swift language for high-performance computing. In this implementation, the Swift script is translated into an MPI program that uses the Turbine (hence, /T) and ADLB runtime libraries for highly scalable dataflow processing over MPI, without single-node bottlenecks. |
SWIG | 2.0.12-IGB-gcc-4.9.4-Python-2.7.13 3.0.12-IGB-gcc-4.9.4-Python-2.7.13 3.0.12-IGB-gcc-4.9.4-Python-2.7.13-Perl-5.24.1 |
SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages. - Homepage: http://www.swig.org/ |
synapseclient | 1.9.2-IGB-gcc-4.9.4-Python-3.6.1 | Synapse is an open source software platform that data scientists can use to carry out, track, and communicate their research in real time. |
Szip | Szip compression software, providing lossless compression of scientific data | |
t3f | 1.1.0-IGB-gcc-4.9.4-Python-3.6.1 1.1.0-IGB-gcc-4.9.4-Python-3.6.1-TF-2.0.3 20200316-IGB-gcc-4.9.4-Python-3.6.1-TF-2.0.3 20200316-IGB-gcc-4.9.4-Python-3.6.1-TF-2.2.0 |
t3f is a library for working with Tensor Train decomposition. |
tabix | 0.2.6-IGB-gcc-4.9.4 | Generic indexer for TAB-delimited genome position files |
tabixpp | 1.1.0-IGB-gcc-8.2.0 | This is a C++ wrapper around tabix project which abstracts some of the details of opening and jumping in tabix-indexed files. |
tagdigger | 1.1-IGB-gcc-4.9.4-Python-3.6.1 | TagDigger is a program for processing FASTQ files from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) experiments. |
tailseeker | 3.2.1 | Tailseeker is the official pipeline for TAIL-seq, which measures poly(A) tail lengths and 3′-end modifications with Illumina SBS sequencers. |
TAPE | 0.4-IGB-gcc-4.9.4-Python-3.6.1 20191209-IGB-gcc-4.9.4-Python-3.6.1 |
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. |
tar | 1.32-IGB-gcc-8.2.0 | tar: The GNU tape archiver |
TASSEL | 5.2.28-Java-1.8.0_121 5.2.28-Java-1.8.0_152 |
While TASSEL has changed considerably since its initial public release in 2001, its primary function continues to be providing tools to investigate the relationship between phenotypes and genotypes |
tbb | Intel Threading Building Blocks 4.0 (Intel TBB) is a widely used, award-winning C++ template library for creating reliable, portable, and scalable parallel applications. Use Intel TBB for a simple and rapid way of developing robust task-based parallel applications that scale to available processor cores, are compatible with multiple environments, and are easier to maintain. Intel TBB is the most proficient way to implement future-proof parallel applications that tap into the power and performance of multicore and manycore hardware platforms. | |
tbl2asn | 20180516 20190117 20200203 20200707 |
Tbl2asn is a command-line program that automates the creation of sequence records for submission to GenBank |
Tcl | Tcl (Tool Command Language) is a very powerful but easy to learn dynamic programming language,suitable for a very wide range of uses, including web and desktop applications, networking, administration,testing and many more. | |
tensorboardX | 1.9-IGB-gcc-4.9.4-Python-3.6.1 2.0-IGB-gcc-4.9.4-Python-3.6.1 2.1-IGB-gcc-8.2.0-Python-3.7.2 2.5.1-IGB-gcc-8.2.0 |
Tensorboard for PyTorch. |
Tensorflow | 1.15.2-IGB-gcc-4.9.4-Python-3.6.1 1.2.1-IGB-gcc-4.9.4-Python-2.7.13 2.2.0-IGB-gcc-4.9.4-Python-3.6.1 2.8.2-IGB-gcc-8.2.0-Python-3.7.2 2.9.1-IGB-gcc-8.2.0-Python-3.7.2 |
An open-source software library for Machine Intelligence |
Tensorflow-GPU | 1.13.1-IGB-gcc-4.9.4-Python-3.6.1 1.14.0-IGB-gcc-4.9.4-Python-3.6.1 1.2.1-IGB-gcc-4.9.4-Python-3.6.1 1.5.1-IGB-gcc-4.9.4-Python-3.6.1 1.9.0-IGB-gcc-4.9.4-Python-3.6.1 2.0.3-IGB-gcc-4.9.4-Python-3.6.1 2.11.0-IGB-gcc-8.2.0-Python-3.7.2 2.2.0-IGB-gcc-4.9.4-Python-3.6.1 2.3.1-IGB-gcc-8.2.0-Python-3.7.2 2.5.3-IGB-gcc-8.2.0-Python-3.7.2 2.6.5-IGB-gcc-8.2.0-Python-3.7.2 2.9.1-IGB-gcc-8.2.0-Python-3.7.2 |
An open-source software library for Machine Intelligence |
TensorFlowModels | 1.11-IGB-gcc-4.9.4-Python-3.6.1 20171113-IGB-gcc-4.9.4-Python-3.6.1 |
This repository contains a number of different models implemented in TensorFlow: |
tensorly | 0.4.2-IGB-gcc-4.9.4-Python-3.6.1 | Simple and Fast Tensor Learning in Python |
TEtranscripts | 2.2.1-IGB-gcc-8.2.0-Python-3.7.2 | TEtranscripts and TEcount takes RNA-seq (and similar data) and annotates reads to both genes & transposable elements. TEtranscripts then performs differential analysis using DESeq2. |
Tk | Tk is an open source, cross-platform widget toolchain that provides a library of basic elements for building a graphical user interface (GUI) in many different programming languages. | |
TMHMM | 2.0c | Prediction of transmembrane helices in proteins |
toil | 5.3.0-IGB-gcc-8.2.0-Python-3.7.2 | Toil is a scalable, efficient, cross-platform (Linux & macOS) pipeline management system, written entirely in Python, and designed around the principles of functional programming. |
tolkein | 0.2.6-IGB-gcc-4.9.4-Python-3.6.1 | Tree of Life Kit of Evolutionary Informatics Novelties |
tombo | 1.5-IGB-gcc-4.9.4-Python-3.6.1 | Tombo is a suite of tools primarily for the identification of modified nucleotides from nanopore sequencing data. Tombo also provides tools for the analysis and visualization of raw nanopore signal. |
TopHat | 1.4.1 2.1.1-IGB-gcc-4.9.4 |
TopHat is a fast splice junction mapper for RNA-Seq reads. |
Torch | 20171018-IGB-gcc-4.9.4-Python-3.6.1 | Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. |
torchsample | 0.1.3-IGB-gcc-4.9.4-Python-3.6.1 | High-Level Training, Data Augmentation, and Utilities for Pytorch |
TransDecoder | 5.1.0-IGB-gcc-4.9.4-Perl-5.24.1 5.5.0-IGB-gcc-4.9.4-Perl-5.24.1 5.7.0-IGB-gcc-8.2.0-Perl-5.28.1 |
TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks. |
transformers | 4.40.2-IGB-gcc-8.2.0-Python-3.10.1 | Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. |
transrate | 1.0.3 | Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. |
tre | 20161208-IGB-gcc-4.9.4 | TRE is a lightweight, robust, and efficient POSIX compliant regexp matching library with some exciting features such as approximate (fuzzy) matching.The matching algorithm used in TRE uses linear worst-case time in the length of the text being searched, and quadratic worst-case time in the length of the used regular expression.In other words, the time complexity of the algorithm is O(M^2N), where M is the length of the regular expression and N is the length of the text. The used space is also quadratic on the length of the regex, but does not depend on the searched string. This quadratic behaviour occurs only on pathological cases which are probably very rare in practice. |
treemix | 1.13-IGB-gcc-4.9.4 | TreeMix is a method for inferring the patterns of population splits and mixtures in the history of a set of populations. In the underlying model, the modern-day populations in a species are related to a common ancestor via a graph of ancestral populations. We use the allele frequencies in the modern populations to infer the structure of this graph. |
trf | 4.0.9 | Tandem Repeats Finder is a program to locate and display tandem repeats in DNA sequences. |
Trimmomatic | 0.33-Java-1.8.0_152 0.36-Java-1.8.0_152 0.38-Java-1.8.0_152 0.39-Java-1.8.0_201 |
Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data.The selection of trimming steps and their associated parameters are supplied on the command line. |
Trim_Galore | 0.4.4-IGB-gcc-4.9.4 0.6.5-IGB-gcc-4.9.4 |
A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files, with some extra functionality for MspI-digested RRBS-type (Reduced Representation Bisufite-Seq) libraries. |
Trinity | 2.10.0-IGB-gcc-8.2.0 2.14.0-IGB-gcc-8.2.0 2.15.1-IGB-gcc-8.2.0 2.4.0-IGB-gcc-4.9.4 2.5.0-IGB-gcc-4.9.4 2.6.5-IGB-gcc-4.9.4 2.8.5-IGB-gcc-4.9.4 |
Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. |
Trinotate | 3.1.1-IGB-gcc-4.9.4-Perl-5.24.1 3.2.1-IGB-gcc-4.9.4-Perl-5.24.1 4.0.0-IGB-gcc-8.2.0-Perl-5.28.1 |
Trinotate is a comprehensive annotation suite designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms. Trinotate makes use of a number of different well referenced methods for functional annotation including homology search to known sequence data (BLAST+/SwissProt), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (signalP/tmHMM), and leveraging various annotation databases (eggNOG/GO/Kegg databases). All functional annotation data derived from the analysis of transcripts is integrated into a SQLite database which allows fast efficient searching for terms with specific qualities related to a desired scientific hypothesis or a means to create a whole annotation report for a transcriptome. |
tRNAscan-SE | 1.3.1-IGB-gcc-4.9.4-Perl-5.26.1-unthreaded | tRNAscan-SE: a tool for finding transfer RNAs |
truesight | 0.06-IGB-gcc-4.9.4 | A Self-training Algorithm for Splice Junction Detection using RNA-seq. |
Trycycler | 0.4.1-IGB-gcc-8.2.0-Python-3.7.2 | Trycycler is a tool for generating consensus long-read assemblies for bacterial genomes. I.e. if you have multiple long-read assemblies for the same isolate, Trycycler can combine them into a single assembly that is better than any of your inputs. |
TSEBRA | 1.0.3-IGB-gcc-8.2.0 | TSEBRA is a combiner tool that selects transcripts from gene predictions based on the support by extrisic evidence in form of introns and start/stop codons. It was developed to combine BRAKER11 and BRAKER22 predicitons to increase their accuracies. |
TULIP | 20170513-IGB-gcc-4.9.4-Perl-5.24.1 | Tulipseed takes as input alignments files of long reads to sparse short seeds, and outputs a graph and scaffold structures. Tulipbulb adds long read sequencing data to these. |
udunits | UDUNITS supports conversion of unit specifications between formatted and binary forms, arithmetic manipulation of units, and conversion of values between compatible scales of measurement. | |
ukbiobank | 20190130 | Biobank download software |
umap-learn | 0.4.6-IGB-gcc-4.9.4-Python-3.6.1 0.4.6-IGB-gcc-8.2.0-Python-3.7.2 0.5.2-IGB-gcc-8.2.0-Python-3.7.2 |
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. |
umi-tools | 1.0.1-IGB-gcc-8.2.0-Python-3.7.2 | Tools for handling Unique Molecular Identifiers in NGS data sets |
unanimity | 20180307-IGB-gcc-4.9.4 | C++ library and its applications to generate and process accurate consensus sequences |
Unicycler | 0.4.3-IGB-gcc-4.9.4-Python-3.6.1 0.4.4-IGB-gcc-4.9.4-Python-3.6.1 0.4.8-IGB-gcc-4.9.4-Python-3.6.1 |
Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly. |
unirep | 20200303-IGB-gcc-4.9.4-Python-3.6.1 | UniRep, a mLSTM "babbler" deep representation learner for protein engineering informatics. |
unrar | 5.7.3-IGB-gcc-4.9.4 | RAR is a powerful archive manager. |
USEARCH | 11.0.667 11.0.667-akent 6.1.544 7.0.1090 9.2.64 |
USEARCH is a unique sequence analysis tool which offers search and clustering algorithms that are often orders of magnitude faster than BLAST. |
util-linux | 2.34-IGB-gcc-4.9.4 | Set of Linux utilities |
valgrind | 3.13.0-IGB-gcc-4.9.4 | Valgrind is an instrumentation framework for building dynamic analysis tools. There are Valgrind tools that can automatically detect many memory management and threading bugs, and profile your programs in detail. You can also use Valgrind to build new tools. |
variscan | 2.0.3-IGB-gcc-4.9.4 | VariScan is a software package for the analysis of DNA sequence polymorphisms at the whole genome scale. Among other features, |
VarScan | 2.3.9-Java-1.8.0_152 | |
vartrix | 1.1.22 | VarTrix is a software tool for extracting single cell variant information from 10x Genomics single cell data. VarTrix will take a set of previously defined variant calls and use that to identify those variants in the single cell data. |
VASP-E | 20190225-IGB-gcc-4.9.4 20191112-IGB-gcc-4.9.4 |
VASP and VASP-E explore the idea that a solid geometric representation of molecular structures can be used to automatically deconstruct proteins into functional elements for the study of binding specificity. |
vcfCodingSnps | 1.5 | vcfCodingSnps is a SNP annotation tool that annotates coding variants in VCF files. |
vcflib | 1.0.0-rc2-IGB-gcc-8.2.0 1.0.2-IGB-gcc-8.2.0 |
a simple C++ library for parsing and manipulating VCF files, + many command-line utilities |
VCFtools | 0.1.15-IGB-gcc-4.9.4-Perl-5.24.1 0.1.16-IGB-gcc-8.2.0-Perl-5.28.1 |
The aim of VCFtools is to provide easily accessible methods for working with complex genetic variation data in the form of VCF files. |
velocyto | 0.17.17-IGB-gcc-4.9.4-Python-3.6.1 | Velocyto is a library for the analysis of RNA velocity. |
velocyto.R | 20190527-IGB-gcc-4.9.4-R-3.4.2 | RNA velocity estimation in R |
velvet | 1.2.10-IGB-gcc-4.9.4-kmer_121 1.2.10-IGB-gcc-8.2.0-kmer_121 |
Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom. |
VEP | 92.4-IGB-gcc-4.9.4-Perl-5.24.1 | VEP (Variant Effect Predictor) predicts the functional effects of genomic variants. |
vg | 1.15.0 | variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods |
VICSIN | 0.5-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 0.5.1-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 0.5.2-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 1.0-IGB-gcc-4.9.4-Perl-5.24.1-Python-2.7.13 dev |
VICSIN = Viral, Integrative, and Conjugative Sequence Identification and Networking |
ViennaRNA | 2.4.14-IGB-gcc-4.9.4 2.4.2-IGB-gcc-4.9.4 |
The ViennaRNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures. |
VirSorter | 20170215-IGB-gcc-4.9.4-Perl-5.24.1 | VirSorter: mining viral signal from microbial genomic data. |
VirSorter2 | 2.2.3 | irSorter2 applies a multi-classifier, expert-guided approach to detect diverse DNA and RNA virus genomes. |
virulencefinder | 2.0.4-IGB-gcc-8.2.0-Python-3.7.2 | The VirulenceFinder service contains one python script virulencefinder.py which is the script of the latest version of the VirulenceFinder service. VirulenceFinder identifies viruelnce genes in total or partial sequenced isolates of bacteria - at the moment only E. coli, Enterococcus, S. aureus and Listeria are available. |
VMD | 1.9.3-IGB-gcc-4.9.4 1.9.4a38-IGB-gcc-8.2.0 |
VMD is a molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting. |
VSEARCH | 2.4.3-IGB-gcc-4.9.4 | VSEARCH stands for vectorized search, as the tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. - Homepage: https://github.com/torognes/vsearch |
weblogo | 3.7.12-IGB-gcc-8.2.0-Python-3.10.1 | WebLogo is a web-based application designed to make the generation of sequence logos easy and painless. WebLogo has been featured in over 10000 scientific publications. |
whatshap | 1.0-IGB-gcc-8.2.0-Python-3.7.2 | WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads. |
wombat | 20210107 | WOMBAT is a program to facilitate analyses fitting a linear, mixed model via restricted maximum likelihood (REML). |
wtdbg2 | 2.3-IGB-gcc-4.9.4 2.5-IGB-gcc-4.9.4 |
Wtdbg2 is a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output. |
wxPython | 4.1.0-IGB-gcc-4.9.4-Python-3.6.1 4.1.0-IGB-gcc-8.2.0-Python-3.7.2 |
the cross-platform GUI toolkit for the Python language. With wxPython software developers can create truly native user interfaces for their Python applications, that run with little or no modifications on Windows, Macs and Linux or other unix-like systems. |
x264 | x264 is a free software library and application for encoding video streams into the H.264/MPEG-4 AVC compression format, and is released under the terms of the GNU GPL. | |
xgboost | 0.80-IGB-gcc-4.9.4-Python-3.6.1 | XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. |
xorg-macros | X.org macros utilities. | |
xpore | 2.1-IGB-gcc-8.2.0-Python-3.10.1 | xPore is a Python package for identification of differentail RNA modifications from Nanopore sequencing data. |
xTea | 0.1.6 | xTea (comprehensive transposable element analyzer) is designed to identify TE insertions from paired-end Illumina reads, barcode linked-reads, long reads (PacBio or Nanopore), or hybrid data from different sequencing platforms and takes whole-exome sequencing (WES) or whole-genome sequencing (WGS) data as input. |
XZ | 5.2.3-IGB-gcc-4.9.4 5.2.3-IGB-gcc-8.2.0 |
xz: XZ utilities |
yahs | 1.2a.2-IGB-gcc-8.2.0 | YaHS is a scaffolding tool using Hi-C data. It relies on a new algothrim for contig joining detection which considers the topological distribution of Hi-C signals aiming to distingush real interaction signals from mapping nosies. |
Yasm | Yasm: Complete rewrite of the NASM assembler with BSD license | |
ZeroMQ | ZeroMQ looks like an embeddable networking library but acts like a concurrency framework. It gives you sockets that carry atomic messages across various transports like in-process, inter-process, TCP, and multicast. You can connect sockets N-to-N with patterns like fanout, pub-sub, task distribution, and request-reply. It is fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built as asynchronous message-processing tasks. It has a score of language APIs and runs on most operating systems. | |
zlib | zlib is designed to be a free, general-purpose, legally unencumbered -- that is, not covered by any patents -- lossless data-compression library for use on virtually any computer hardware and operating system. | |
zorro | 20111201 | A probabilistic alignment masking program |
zstd | 1.4.4-IGB-gcc-4.9.4 1.5.5-IGB-gcc-8.2.0 |
Zstandard is a real-time compression algorithm, providing high compression ratios. It offers a very wide range of compression/speed trade-off, while being backed by a very fast decoder. It also offers a special mode for small data, called dictionary compression, and can create dictionaries from any sample set. |