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http://www.igbam.bilgem.tubitak.gov.tr/softwares/HomSI/

A software tool that identifies homozygous regions using deep sequence data.

Proper citation: HomSI (RRID:SCR_010771) Copy   

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http://crac.gforge.inria.fr/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Integrated RNA-Seq read analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CRAC (RRID:SCR_010652) Copy   

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http://mutpred.mutdb.org/

Web application tool developed to classify an amino acid substitution as disease-associated or neutral in human.

Proper citation: MutPred (RRID:SCR_010778) Copy   

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http://www.broadinstitute.org/cancer/cga/mutsig

Software that analyzes lists of mutations discovered in DNA sequencing, to identify genes that were mutated more often than expected by chance given background mutation processes.

Proper citation: MutSig (RRID:SCR_010779) Copy   

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http://snpanalyzer.uthsc.edu/

A tool to predict whether a nonsynonymous single nucleotide polymorphism (nsSNP) has a phenotypic effect.

Proper citation: nsSNPAnalyzer (RRID:SCR_010780) Copy   

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https://www.broadinstitute.org/scientific-community/science/programs/genome-sequencing-and-analysis/computational-rd/computational-

Software tool as whole genome shotgun assembler that can generate high quality genome assemblies using short reads (~100bp) such as those produced by the new generation of sequencers.

Proper citation: ALLPATHS-LG (RRID:SCR_010742) Copy   

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http://www.geneious.com/

Software package for sequence alignment, assembly and analysis. Integrated and extendable desktop software platform for organization and analysis of sequence data. Bioinformatics software platform packed with molecular biology and sequence analysis tools.

Proper citation: Geneious (RRID:SCR_010519) Copy   

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http://soap.genomics.org.cn/soapdenovo.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 24,2023. Software tool for de novo assembly of human genomes with massively parallel short read sequencing.Short-read assembly method that can build de novo draft assembly for human sized genomes.Software package for assembling short oligonucleotide into contigs and scaffolds., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOAPdenovo (RRID:SCR_010752) Copy   

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http://www.molecularevolution.org/software/genomics/velvet

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package as de novo genomic assembler for short read sequencing technologies using de Bruijn graphs. Takes in short read sequences, removes errors, then produces high quality unique contigs, retrieves repeated areas between contigs. Can leverage very short reads in combination with read pairs to produce useful assemblies. Operating system Unix/Linux., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Velvet (RRID:SCR_010755) Copy   

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https://www.hgsc.bcm.edu/content/atlas2

A next-generation sequencing suite of variant analysis tools specializing in the separation of true SNPs and insertions and deletions (indels) from sequencing and mapping errors in WECS data.

Proper citation: Atlas2 (RRID:SCR_010756) Copy   

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http://newt-omics.mpi-bn.mpg.de/index.php

Newt-omics is a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centered database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ~50,000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13,810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise. The newt Notopthalmus viridescens is the master of regeneration. This organism is known for more than 200 years for its exceptional regenerative capabilities. Newts can completely replace lost appendages like limb and tail, lens and retina and parts of the central nervous system. Moreover, after cardiac injury newts can rebuild the functional myocardium with no scar formation. To date only very limited information from public databases is available. Newt-Omics aims to provide a comprehensive platform of expressed genes during tissue regeneration, including extensive annotations, expression data and experimentally verified peptide sequences with yet no homology to other publicly available gene sequences. The goal is to obtain a detailed understanding of the molecular processes underlying tissue regeneration in the newt, that may lead to the development of approaches, efficiently stimulating regenerative pathways in mammalians. * Number of contigs: 26594 * Number of est in contigs: 48537 * Number of transcripts with verified peptide: 5291 * Number of peptides: 15169

Proper citation: Newtomics (RRID:SCR_006073) Copy   

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http://www.nematodes.org/nembase4/

NEMBASE is a comprehensive Nematode Transcriptome Database including 63 nematode species, over 600,000 ESTs and over 250,000 proteins. Nematode parasites are of major importance in human health and agriculture, and free-living species deliver essential ecosystem services. The genomics revolution has resulted in the production of many datasets of expressed sequence tags (ESTs) from a phylogenetically wide range of nematode species, but these are not easily compared. NEMBASE4 presents a single portal into extensively functionally annotated, EST-derived transcriptomes from over 60 species of nematodes, including plant and animal parasites and free-living taxa. Using the PartiGene suite of tools, we have assembled the publicly available ESTs for each species into a high-quality set of putative transcripts. These transcripts have been translated to produce a protein sequence resource and each is annotated with functional information derived from comparison with well-studied nematode species such as Caenorhabditis elegans and other non-nematode resources. By cross-comparing the sequences within NEMBASE4, we have also generated a protein family assignment for each translation. The data are presented in an openly accessible, interactive database. An example of the utility of NEMBASE4 is that it can examine the uniqueness of the transcriptomes of major clades of parasitic nematodes, identifying lineage-restricted genes that may underpin particular parasitic phenotypes, possible viral pathogens of nematodes, and nematode-unique protein families that may be developed as drug targets.

Proper citation: NEMBASE (RRID:SCR_006070) Copy   

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http://aias.biol.uoa.gr/OMPdb/

A database of Beta-barrel outer membrane proteins from Gram-negative bacteria. The web interface of OMPdb offers the user the ability not only to view the available data, but also to submit advanced queries for text search within the database''s protein entries or run BLAST searches against the database. The most up-to-date version of the database (as well as all past versions) can be downloaded in various formats (flat text, XML format or raw FASTA sequences). For constructing OMPdb, multiple freely accessible resources were combined and a detailed literature search was performed. The classification of OMPdb''s protein entries into families is based mainly on structural and functional criteria. Information included in the database consists of sequence data, as well as annotation for structural characteristics (such as the transmembrane segments), literature references and links to other public databases, features that are unique worldwide. Along with the database, a collection of profile Hidden Markov Models that were shown to be characteristic for Beta-barrel outer membrane proteins was also compiled. This set, when used in combination with our previously developed algorithms (PRED-TMBB, MCMBB and ConBBPRED) will serve as a powerful tool in matters of discrimination and classification of novel Beta-barrel proteins and whole-genome analyses., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: OMPdb (RRID:SCR_006221) Copy   

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http://www.bioconductor.org/packages/devel/bioc/html/deepSNV.html

Software package that provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs (single nucleotide variants).

Proper citation: deepSNV (RRID:SCR_006214) Copy   

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http://evolution.genetics.washington.edu/phylip.html

A free package of software programs for inferring phylogenies (evolutionary trees). The source code is distributed (in C), and executables are also distributed. In particular, already-compiled executables are available for Windows (95/98/NT/2000/me/xp/Vista), Mac OS X, and Linux systems. Older executables are also available for Mac OS 8 or 9 systems.

Proper citation: PHYLIP (RRID:SCR_006244) Copy   

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http://stormo.wustl.edu/ScerTF

Catalog of over 1,200 position weight matrices (PWMs) for 196 different yeast transcription factors (TFs). They've curated 11 literature sources, benchmarked the published position-specific scoring matrices against in-vivo TF occupancy data and TF deletion experiments, and combined the most accurate models to produce a single collection of the best performing weight matrices for Saccharomyces cerevisiae. ScerTF is useful for a wide range of problems, such as linking regulatory sites with transcription factors, identifying a transcription factor based on a user-input matrix, finding the genes bound/regulated by a particular TF, and finding regulatory interactions between transcription factors. Enter a TF name to find the recommended matrix for a particular TF, or enter a nucleotide sequence to identify all TFs that could bind a particular region.

Proper citation: ScerTF (RRID:SCR_006121) Copy   

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http://www-bionet.sscc.ru/sitex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2019. Analyzing protein structure projection on exon-intron structure of corresponding gene through years led to several fundamental conclusions about structural and functional organization of the protein. According to these results we decided to map the protein functional sites. So we created the database SitEx that keep the information about this mapping and included the BLAST search and 3D similar structure search using PDB3DScan for the polypeptide encoded by one exon, participating in organizing the functional site. This will help: # to study the positions of the functional sites in exon structure; # to make the complex analysis of the protein function; # to exposure the exons that took part in exon shuffling and came from bacterial genomes; # to study the peculiarities of coding the polypeptide structures. Currently, SitEx contains information about 9994 functional sites presented in 2021 proteins described in proteomes of 17 organisms.

Proper citation: SitEx (RRID:SCR_006122) Copy   

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http://bio-bigdata.hrbmu.edu.cn/diseasemeth/

Human disease methylation database. DiseaseMeth version 2.0 is focused on aberrant methylomes of human diseases. Used for understanding of DNA methylation driven human diseases.

Proper citation: DiseaseMeth (RRID:SCR_005942) Copy   

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http://distild.jensenlab.org/

The DistiLD database aims to increase the usage of existing genome-wide association studies (GWAS) results by making it easy to query and visualize disease-associated SNPs and genes in their chromosomal context. The database performs three important tasks: # published GWAS are collected from several sources and linked to standardized, international disease codes ICD10 codes) # data from the International HapMap Project are analyzed to define linkage disequilibrium (LD) blocks onto which SNPs and genes are mapped # the web interface makes it easy to query and visualize disease-associated SNPs and genes within LD blocks. Users can query the database by diseases, SNPs or genes. No matter which of the three query modes was used, an intermediate page will be shown listing all the studies that matched the search with a link to the corresponding publication. The user can select either all studies related to a certain disease or one specific study for which to view the related LD blocks. The DistiLD resource integrates information on: * Associations between Single Nucleotide Polymorphisms (SNPs) and diseases from genome-wide association studies (GWAS) * Links between SNPs and genes based on linkage disequilibrium (LD) data from HapMap For convenience, we provide the complete datasets as two (zipped) tab-delimited files. The first file contains GWAS results mapped to LD blocks. The second file contains all SNPs and genes assigned to each LD block.

Proper citation: DistiLD - Diseases and Traits in LD (RRID:SCR_005943) Copy   

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https://compbio.dfci.harvard.edu/predictivenetworks//

A flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ''known'' interactions together with gene expression data to infer robust gene networks. The regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.

Proper citation: Predictive Networks (RRID:SCR_006110) Copy   

•   Source: SciCrunch Registry