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http://www.cbcb.umd.edu/software/phymm/

Software for Phylogenetic Classification of Metagenomic Data with Interpolated Markov Models to taxonomically classify DNA sequences and accurately classify reads as short as 100 bp. PhymmBL, the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Proper citation: Phymm and PhymmBL (RRID:SCR_004751) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/destruct/

A software tool for identifying structural variation in tumour genomes from whole genome illumina sequencing.

Proper citation: deStruct (RRID:SCR_004747) Copy   

•   Source: SciCrunch Registry

http://www.brown.edu/Research/Istrail_Lab/hapcompass.php

Software that utilizes a fast cycle basis algorithm for the accurate haplotype assembly of sequence data. It is able to create pairwise SNP phasings.

Proper citation: HapCompass (RRID:SCR_000942) Copy   

•   Source: SciCrunch Registry

http://www.biobase-international.com/product/genome-trax

Service that provides a comprehensive compilation of variant knowledge that allows you to identify pathogenic variants in human whole genome or exome sequences. It makes it easy to upload a complete genome?s worth of variations and identify the biologically relevant subset of known mutations, mutations that are novel and appear in a candidate disease genes, or mutations that are predicted to have a deleterious effect. The database includes a comprehensive collection of disease causing mutations from HGMD Professional, regulatory sites from TRANSFAC , and disease genes, drug targets and pathways from PROTEOME, as well as pharmacogenomic variants. It integrates the best public data-sets on somatic mutations, allele frequencies and clinical variants, in their most up-to-date version, for a total of more than 165 million annotations. It is possible to identify known pathogenic variants, remove harmless common variants, and obtain deleterious predictions for novel variants. With family data, it is possible to identify variants that are de novo, compound heterozygous only in the offspring. All of the results can be downloaded to Excel for further review. For core facilities and bioinformaticians, the complete underlying data is made available for download and easy integration into custom analysis pipelines. Genome Trax data is optimized to work with many other software packages, such as ANNOVARTM, CLC bio, Alamut, SimulConsult, and Cartagenia.

Proper citation: Genome Trax (RRID:SCR_001234) Copy   

•   Source: SciCrunch Registry

http://www.zbh.uni-hamburg.de/?id=211

A collection of flexible and memory-efficient software programs for k-mer counting and indexing of large sequence sets. It is based on enhanced suffix arrays which gives a much larger flexibility concerning the choice of the k-mer size. It can process large data sizes of several billion bases.

Proper citation: TALLYMER (RRID:SCR_001244) Copy   

•   Source: SciCrunch Registry

https://dalexander.github.io/admixture/download.html

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. It uses a block relaxation approach to alternately update allele frequency and ancestry fraction parameters. Each block update is handled by solving a large number of independent convex optimization problems, which are tackled using a fast sequential quadratic programming algorithm. Convergence of the algorithm is accelerated using a novel quasi-Newton acceleration method., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ADMIXTURE (RRID:SCR_001263) Copy   

•   Source: SciCrunch Registry

http://med.stanford.edu/tanglab/software/frappe.html

Software using a f frequentist approach for estimating individual ancestry proportion.

Proper citation: frappe (RRID:SCR_001264) Copy   

•   Source: SciCrunch Registry

https://www.wtccc.org.uk/

Consortium of 50 research groups across the UK to harness the power of newly-available genotyping technologies to improve our understanding of the aetiological basis of several major causes of global disease. The consortium has gathered genotype data for up to 500,000 sites of genome sequence variation (single nucleotide polymorphisms or SNPs) in samples ascertained for the disease phenotypes. Analysis of the genome-wide association data generated has lead to the identification of many SNPs and genes showing evidence of association with disease susceptibility, some of which will be followed up in future studies. In addition, the Consortium has gained important insights into the technical, analytical, methodological and biological aspects of genome-wide association analysis. The core of the study comprised an analysis of 2,000 samples from each of seven diseases (type 1 diabetes, type 2 diabetes, coronary heart disease, hypertension, bipolar disorder, rheumatoid arthritis and Crohn's disease). For each disease, the case samples have been ascertained from sites widely distributed across Great Britain, allowing us to obtain considerable efficiencies by comparing each of these case populations to a common set of 3,000 nationally-ascertained controls also from England, Scotland and Wales. These controls come from two sources: 1,500 are representative samples from the 1958 British Birth Cohort and 1,500 are blood donors recruited by the three national UK Blood Services. One of the questions that the WTCCC study has addressed relates to the relative merits of these alternative strategies for the generation of representative population cohorts. Genotyping for this main Case Control study was conducted by Affymetrix using the (commercial) Affymetrix 500K chip. As part of this study a total of 17,000 samples were typed for 500,000 SNPs. There are two additional components to the study. First, the WTCCC award is part-funding a study of host resistance to infectious diseases in African populations. The same approach has been used to type 2,000 cases of tuberculosis (TB) and 2,000 cases of malaria, as well as 2,000 shared controls. As well as addressing diseases of major global significance, and extending WTCCC coverage into the area of infectious disease, the inclusion of samples of African origin has obvious benefits with respect to methodological aspects of genome-wide association analysis. Second, the WTCCC has, for four additional diseases (autoimmune thyroid disease, breast cancer, ankylosing spondylitis, multiple sclerosis), completed an analysis of 15,000 SNPs designed to represent a large proportion of the known non-synonymous coding SNPs across the genome. This analysis has been performed at the WTSI using a custom Infinium chip (Illumina). Data release The genotypic data of the control samples (1958 British Birth Cohort and UK Blood Service) and from seven diseases analyzed in the main study are now available to qualified researchers. Summary genotype statistics for these collections are available directly from the website. Access to the individual-level genotype data and summary genotype statistics is by application to the Consortium Data Access Committee (CDAC) and approval subject to a Data Access Agreement. WTCCC2: A further round of GWA studies were funded in April 2008. These include 15 WTCCC-collaborative studies and 12 independent studies be supported totaling approximately 120,000 samples. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC2 will perform genome-wide association studies in 13 disease conditions: Ankylosing spondylitis, Barrett's oesophagus and oesophageal adenocarcinoma, glaucoma, ischaemic stroke, multiple sclerosis, pre-eclampsia, Parkinson's disease, psychosis endophenotypes, psoriasis, schizophrenia, ulcerative colitis and visceral leishmaniasis. WTCCC2 will also investigate the genetics of reading and mathematics abilities in children and the pharmacogenomics of statin response. Over 60,000 samples will be analyzed using either the Affymetrix v6.0 chip or the Illumina 660K chip. The WTCCC2 will also genotype 3,000 controls each from the 1958 British Birth cohort and the UK Blood Service control group, and the 6,000 controls will be genotyped on both the Affymetrix v6.0 and Illumina 1.2M chips. WTCCC3: The Wellcome Trust has provided support for a further round of GWA studies in January 2009. These include 5 WTCCC-collaborative studies to be carried out in WTCCC3 and 5 independent studies, across a range of diseases. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC3 will perform genome-wide association studies in the following 4 disease conditions: primary biliary cirrhosis, anorexia nervosa, pre-eclampsia in UK subjects, and the interactions between donor and recipient DNA related to early and late renal transplant dysfunction. The WTCCC3 will also carry out a pilot in a study of the genetics of host control of HIV-1 infection. Over 40,000 samples will be analyzed using the Illumina 660K chip. The WTCCC3 will utilize the 6,000 control genotypes generated by the WTCCC2.

Proper citation: Wellcome Trust Case Control Consortium (RRID:SCR_001973) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/tbrowse/

Software providing a HTML5/javascript based browser for visualizing RNA-seq results in the familiar track layout of common genome browser. But given the quantitative nature of RNA-seq data, in addition to visualizing sequence coverage, the browser quantitates transcript abundance across regions of interest. The HTML5 functionality is made of use to render all the tracks using the canvas drawing element. This greatly reduces the load on servers and allows for rich interactive graphics without the need for third-party plugins. Furthermore, this framework completely segregates data from visualization, making development much easier. The browser is designed to run on all modern browsers: Firefox, Safari, Chrome, Opera and Internet Explorer (though not recommended).

Proper citation: tbrowse (RRID:SCR_001918) Copy   

•   Source: SciCrunch Registry

http://www.biomol-informatics.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 12, 2021. Technology based company in Madrid that offers consulting services on Bioinformatics in areas of research, diagnostics and pharmaceutical industry.

Proper citation: Biomol-Informatics (RRID:SCR_004081) Copy   

•   Source: SciCrunch Registry

http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/

A stand-alone software program for scaffolding pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads.

Proper citation: SSPACE (RRID:SCR_005056) Copy   

•   Source: SciCrunch Registry

http://www.biomedcentral.com/1471-2105/13/189

An algorithm to use optical map information directly within the de Bruijn graph framework to help produce an accurate assembly of a genome that is consistent with the optical map information provided. AGORA takes as input two data structures: OpMap ? an ordered list of fragment sizes representing the optical map; and Edges ? a list of de Bruijn graph edges with their corresponding sequences.

Proper citation: AGORA (RRID:SCR_005070) Copy   

•   Source: SciCrunch Registry

https://github.com/tk2/RetroSeq

A tool for discovery and genotyping of transposable element variants (TEVs) (also known as mobile element insertions) from next-gen sequencing reads aligned to a reference genome in BAM format. The goal is to call TEVs that are not present in the reference genome but present in the sample that has been sequenced. It should be noted that RetroSeq can be used to locate any class of viral insertion in any species where whole-genome sequencing data with a suitable reference genome is available. RetroSeq is a two phase process, the first being the read pair discovery phase where discorandant mate pairs are detected and assigned to a TE class (Alu, SINE, LINE, etc.) by using either the annotated TE elements in the reference and/or aligned with Exonerate to the supplied library of viral sequences.

Proper citation: RetroSeq (RRID:SCR_005133) Copy   

•   Source: SciCrunch Registry

http://bioinfo.mc.vanderbilt.edu/VirusFinder/

Software tool for efficient and accurate detection of viruses and their integration sites in host genomes through next generation sequencing data. Specifically, it detects virus infection, co-infection with multiple viruses, virus integration sites in host genomes, as well as mutations in the virus genomes. It also facilitates virus discovery by reporting novel contigs, long sequences assembled from short reads that map neither to the host genome nor to the genomes of known viruses. VirusFinder 2 works with both paired-end and single-end data, unlike the previous 1.x versions that accepted only paired-end reads. The types of NGS data that VirusFinder 2 can deal with include whole genome sequencing (WGS), whole transcriptome sequencing (RNA-Seq), targeted sequencing data such as whole exome sequencing (WES) and ultra-deep amplicon sequencing.

Proper citation: VirusFinder (RRID:SCR_005205) Copy   

•   Source: SciCrunch Registry

http://stothard.afns.ualberta.ca/downloads/NGS-SNP/

A collection of command-line scripts for providing rich annotations for SNPs identified by the sequencing of transcripts or whole genomes from organisms with reference sequences in Ensembl. Included among the annotations, several of which are not available from any existing SNP annotation tools, are the results of detailed comparisons with orthologous sequences. These comparisons allow, for example, SNPs to be sorted or filtered based on how drastically the SNP changes the score of a protein alignment. Other fields indicate the names of overlapping protein domains or features, and the conservation of both the SNP site and flanking regions. NCBI, Ensembl, and Uniprot IDs are provided for genes, transcripts, and proteins when applicable, along with Gene Ontology terms, a gene description, phenotypes linked to the gene, and an indication of whether the SNP is novel or known. A ?Model_Annotations? field provides several annotations obtained by transferring in silico the SNP to an orthologous gene, typically in a well-characterized species.

Proper citation: NGS-SNP (RRID:SCR_005182) Copy   

•   Source: SciCrunch Registry

http://ergatis.sourceforge.net/

A web interface and scalable software system for bioinformatics workflows that is used to create, run, and monitor reusable computational analysis pipelines. It contains pre-built components for common bioinformatics analysis tasks. These components can be arranged graphically to form highly-configurable pipelines. Each analysis component supports multiple output formats, including the Bioinformatic Sequence Markup Language (BSML). The current implementation includes support for data loading into project databases following the CHADO schema, a highly normalized, community-supported schema for storage of biological annotation data. Ergatis uses the Workflow engine to process its work on a compute grid. Workflow provides an XML language and processing engine for specifying the steps of a computational pipeline. It provides detailed execution status and logging for process auditing, facilitates error recovery from point of failure, and is highly scalable with support for distributed computing environments. The XML format employed enables commands to be run serially, in parallel, and in any combination or nesting level.

Proper citation: Ergatis (RRID:SCR_005377) Copy   

•   Source: SciCrunch Registry

http://toolshed.g2.bx.psu.edu/repository/display_tool?repository_id=5d0de444b1f9ac52&tool_config=database%2Fcommunity_files%2F000%2Frepo_136%2Fcrest.xml&changeset_revision=4f6952e0af48

An algorithm for detecting genomic structural variations at base-pair resolution using next-generation sequencing data. CREST uses pieces of DNA called soft clips to find structural variations. Soft clips are the DNA segments produced during sequencing that fail to properly align to the reference genome as the sample genome is reassembled. CREST uses the soft clips to precisely identify sites of chromosomal rearrangement or where pieces of DNA are inserted or deleted.

Proper citation: CREST (RRID:SCR_005257) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/molbiolib/

A compact, portable, and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. It is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib includes programs to perform a wide variety of analysis tasks such as computing read coverage, annotating genomic intervals, and novel peak calling with a wavelet algorithm. This package assumes fluency in both UNIX and C++.

Proper citation: MolBioLib (RRID:SCR_005372) Copy   

•   Source: SciCrunch Registry

http://www.bioextract.org/GuestLogin

An open, web-based system designed to aid researchers in the analysis of genomic data by providing a platform for the creation of bioinformatic workflows. Scientific workflows are created within the system by recording tasks performed by the user. These tasks may include querying multiple, distributed data sources, saving query results as searchable data extracts, and executing local and web-accessible analytic tools. The series of recorded tasks can then be saved as a reproducible, sharable workflow available for subsequent execution with the original or modified inputs and parameter settings. Integrated data resources include interfaces to the National Center for Biotechnology Information (NCBI) nucleotide and protein databases, the European Molecular Biology Laboratory (EMBL-Bank) non-redundant nucleotide database, the Universal Protein Resource (UniProt), and the UniProt Reference Clusters (UniRef) database. The system offers access to numerous preinstalled, curated analytic tools and also provides researchers with the option of selecting computational tools from a large list of web services including the European Molecular Biology Open Software Suite (EMBOSS), BioMoby, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The system further allows users to integrate local command line tools residing on their own computers through a client-side Java applet.

Proper citation: BioExtract (RRID:SCR_005397) Copy   

•   Source: SciCrunch Registry

http://splitread.sourceforge.net/

Software for detecting INDELs (small insertions and deletion with size less than 50bp) as well as large deletions that are within the coding regions from the exome sequencing data. It also can be applied to the whole genome sequencing data.

Proper citation: SPLITREAD (RRID:SCR_005264) Copy   

•   Source: SciCrunch Registry