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http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

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http://www.seqexpress.com/

A comprehensive analysis and visualization software package for gene expression experiments that provides: a number of clustering and analysis techniques; integrated gene expression and analysis result visualizations, integration with the Gene Expression Omnibus; and an optional data sharing architecture. GO is used to assign functional enrichment scores to clusters, using a combination of specially developed techniques and general statistical methods. These results can be explored using the in built ontology browsing tool or through the generated web pages. SeqExpress also supports numerous data transformation, projection, visualization, file export/import, searching, integration (with R), and clustering options.

Proper citation: SeqExpress (RRID:SCR_007075) Copy   

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https://cran.r-project.org/web/packages/ibdreg/index.html

Software package in S-PLUS and R to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree. (entry from Genetic Analysis Software)

Proper citation: IBDREG (RRID:SCR_013127) Copy   

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https://github.com/lufuhao/Gsnap2Augustus

Software tool to generate hints for Augustus in ab initio gene prediction using 2 step mapping by Gsnap.

Proper citation: Gsnap2Augustus (RRID:SCR_017555) Copy   

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https://cran.r-project.org/web/packages/tdthap/index.html

Software package for TDT with extended haplotypes in the R language. R is the public domain dialect of S. It should be possible to port this library to the commercial Splus product. The main problem would be translation of the help files. (entry from Genetic Analysis Software)

Proper citation: R/TDTHAP (RRID:SCR_007625) Copy   

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http://www.loni.usc.edu/BIRN/Projects/Mouse/

Animal model data primarily focused on mice including high resolution MRI, light and electron microscopic data from normal and genetically modified mice. It also has atlases, and the Mouse BIRN Atlasing Toolkit (MBAT) which provides a 3D visual interface to spatially registered distributed brain data acquired across scales. The goal of the Mouse BIRN is to help scientists utilize model organism databases for analyzing experimental data. Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/). The Mouse BIRN testbeds initially focused on mouse models of neurodegenerative diseases. Mouse BIRN testbed partners provide multi-modal, multi-scale reference image data of the mouse brain as well as genetic and genomic information linking genotype and brain phenotype. Researchers across six groups are pooling and analyzing multi-scale structural and functional data and integrating it with genomic and gene expression data acquired from the mouse brain. These correlated multi-scale analyses of data are providing a comprehensive basis upon which to interpret signals from the whole brain relative to the tissue and cellular alterations characteristic of the modeled disorder. BIRN's infrastructure is providing the collaborative tools to enable researchers with unique expertise and knowledge of the mouse an opportunity to work together on research relevant to pre-clinical mouse models of neurological disease. The Mouse BIRN also maintains a collaborative Web Wiki, which contains announcements, an FAQ, and much more.

Proper citation: Mouse Biomedical Informatics Research Network (RRID:SCR_003392) Copy   

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https://www.hgmd.cf.ac.uk/ac/introduction.php?lang=english

Curated database of known (published) gene lesions responsible for human inherited disease.

Proper citation: Human Gene Mutation Database (RRID:SCR_001621) Copy   

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http://www.e-cell.org/

Software platform, general technologies and theoretical supports for computational biology with the grand aim to make precise whole cell simulation at the molecular level possible.Technologies include formalisms and techniques, including technologies to predict, obtain or estimate parameters such as reaction rates and concentrations of molecules in the cell. The E-Cell System is a software platform for modeling, simulation and analysis of complex, heterogeneous and multi-scale system like the cell. The E-Cell Project is open to anyone who shares the view with u that development of cell simulation technology, and, even if such ultimate goal might not be within ten years of reach yet, solving various conceptual, computational and experimental problems that will continue to arise in the course of pursuing it, may have a multitude of eminent scientific, medical and engineering impacts on our society.

Proper citation: Electronic Cell Project (RRID:SCR_007381) Copy   

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http://folk.uio.no/thoree/FEST/

An R package for simulations and likelihood calculations of pair-wise family relationships using DNA marker data. (entry from Genetic Analysis Software)

Proper citation: R/FEST (RRID:SCR_013347) Copy   

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http://chgv.org/GenicIntolerance/

A gene-based score intended to help in the interpretation of human sequence data. The score is designed to rank genes in terms of whether they have more or less common functional genetic variation relative to the genome wide expectation given the amount of apparently neutral variation the gene has. A gene with a positive score has more common functional variation, and a gene with a negative score has less and is referred to as intolerant.

Proper citation: Residual Variation Intolerance Score (RVIS) (RRID:SCR_013850) Copy   

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https://picrust.github.io/picrust/

Software package to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. Used to predict which gene families are present and then combines gene families to estimate the composite metagenome.

Proper citation: PICRUSt (RRID:SCR_016855) Copy   

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http://bioweb.ensam.inra.fr/esther

Database and tools for analysis of protein and nucleic acid sequences belonging to superfamily of alpha/beta hydrolases homologous to cholinesterases. Covers multiple species, including human, mouse caenorhabditis and drosophila., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ESTHER (RRID:SCR_002621) Copy   

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http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

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http://scicrunch.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2019.

Database for those interested in the consequences of Factor VIII genetic variation at the DNA and protein level, it provides access to data on the molecular pathology of haemophilia A. The database presents a review of the structure and function of factor VIII and the molecular genetics of haemophilia A, a real time update of the biostatistics of each parameter in the database, a molecular model of the A1, A2 and A3 domains of the factor VIII protein (based on the crystal structure of caeruloplasmin) and a bulletin board for discussion of issues in the molecular biology of factor VIII. The database is completely updated with easy submission of point mutations, deletions and insertions via e-mail of custom-designed forms. A methods section devoted to mutation detection is available, highlighting issues such as choice of technique and PCR primer sequences. The FVIII structure section now includes a download of a FVIII A domain homology model in Protein Data Bank format and a multiple alignment of the FVIII amino-acid sequences from four species (human, murine, porcine and canine) in addition to the virtual reality simulations, secondary structural data and FVIII animation already available. Finally, to aid navigation across this site, a clickable roadmap of the main features provides easy access to the page desired. Their intention is that continued development and updating of the site shall provide workers in the fields of molecular and structural biology with a one-stop resource site to facilitate FVIII research and education. To submit your mutants to the Haemophilia A Mutation Database email the details. (Refer to Submission Guidelines)

Proper citation: HAMSTeRS - The Haemophilia A Mutation Structure Test and Resource Site (RRID:SCR_006883) Copy   

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http://sourceforge.net/p/fastsemsim/home/Home/

A package that implements several semantic similarity measures. It is both a library and an end-user application, featuring an intuitive graphical user interface (GUI). It has been implemented with the aim of being fast, expandable, and easy to use. It allows the user to work with the most updated version of GO database and customizable annotation corpora. It provides a set of logically-organized classes that can be easily exploited to both integrate semantic similarity into different analysis pipelines and extend the library with new measures. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FastSemSim (RRID:SCR_006919) Copy   

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http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

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http://www.europhenome.org

Open source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.

Proper citation: Europhenome Mouse Phenotyping Resource (RRID:SCR_006935) Copy   

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http://autismkb.cbi.pku.edu.cn/

Genetic factors contribute significantly to ASD. AutismKB is an evidence-based knowledgebase of Autism spectrum disorder (ASD) genetics. The current version contains 2193 genes (99 syndromic autism related genes and 2135 non-syndromic autism related genes), 4617 Copy Number Variations (CNVs) and 158 linkage regions associated with ASD by one or more of the following six experimental methods: # Genome-Wide Association Studies (GWAS); # Genome-wide CNV studies; # Linkage analysis; # Low-scale genetic association studies; # Expression profiling; # Other low-scale gene studies. Based on a scoring and ranking system, 99 syndromic autism related genes and 383 non-syndromic autism related genes (434 genes in total) were designated as having high confidence. Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 1.0-2.6%. The three core symptoms of ASD are: # impairments in reciprocal social interaction; # communication impairments; # presence of restricted, repetitive and stereotyped patterns of behavior, interests and activities.

Proper citation: AutismKB (RRID:SCR_006937) Copy   

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http://redfly.ccr.buffalo.edu

Curated collection of known Drosophila transcriptional cis-regulatory modules (CRMs) and transcription factor binding sites (TFBSs). Includes experimentally verified fly regulatory elements along with their DNA sequence, associated genes, and expression patterns they direct. Submission of experimentally verified cis-regulatory elements that are not included in REDfly database are welcome.

Proper citation: REDfly Regulatory Element Database for Drosophilia (RRID:SCR_006790) Copy   

•   Source: SciCrunch Registry

http://www.nervenet.org/main/dictionary.html

A mouse-related portal of genomic databases and tables of mouse brain data. Most files are intended for you to download and use on your own personal computer. Most files are available in generic text format or as FileMaker Pro databases. The server provides data extracted and compiled from: The 2000-2001 Mouse Chromosome Committee Reports, Release 15 of the MIT microsatellite map (Oct 1997), The recombinant inbred strain database of R.W. Elliott (1997) and R. W. Williams (2001), and the Map Manager and text format chromosome maps (Apr 2001). * LXS genotype (Excel file): Updated, revised positions for 330 markers genotyped using a panel of 77 LXS strain. * MIT SNP DATABASE ONLINE: Search and sort the MIT Single Nucleotide Polymorphism (SNP) database ONLINE. These data from the MIT-Whitehead SNP release of December 1999. * INTEGRATED MIT-ROCHE SNP DATABASE in EXCEL and TEXT FORMATS (1-3 MB): Original MIT SNPs merged with the new Roche SNPs. The Excel file has been formatted to illustrate SNP haplotypes and genetic contrasts. Both files are intended for statistical analyses of SNPs and can be used to test a method outlined in a paper by Andrew Grupe, Gary Peltz, and colleagues (Science 291: 1915-1918, 2001). The Excel file includes many useful equations and formatting that will help in navigating through this large database and in testing the in silico mapping method. * Use of inbred strains for the study of individual differences in pain related phenotypes in the mouse: Elissa J. Chesler''s 2002 dissertation, discussing issues relevant to the integration of genomic and phenomic data from standard inbred strains including genetic interactions with laboratory environmental conditions and the use of various in silico inbred strain haplotype based mapping algorithms for QTL analysis. * SNP QTL MAPPER in EXCEL format (572 KB, updated January 2002 by Elissa Chesler): This Excel workbook implements the Grupe et al. mapping method and outputs correlation plots. The main spreadsheet allows you to enter your own strain data and compares them to haplotypes. Be very cautious and skeptical when using this spreadsheet and the technique. Read all of the caveates. This excel version of the method was developed by Elissa Chesler. This updated version (Jan 2002) handles missing data. * MIT SNP Database (tab-delimited text format): This file is suitable for manipulation in statistics and spreadsheet programs (752 KB, Updated June 27, 2001). Data have been formatted in a way that allows rapid acquisition of the new data from the Roche Bioscience SNP database. * MIT SNP Database (FileMaker 5 Version): This is a reformatted version of the MIT Single Nucleotide Polymorphism (SNP) database in FileMaker 5 format. You will need a copy of this application to open the file (Mac and Windows; 992 KB. Updated July 13, 2001 by RW). * Gene Mapping and Map Manager Data Sets: Genetic maps of mouse chromosomes. Now includes a 10th generation advanced intercross consisting of 500 animals genetoyped at 340 markers. Lots of older files on recombinant inbred strains. * The Portable Dictionary of the Mouse Genome, 21,039 loci, 17,912,832 bytes. Includes all 1997-98 Chromosome Committee Reports and MIT Release 15. * FullDict.FMP.sit: The Portable Dictionary of the Mouse Genome. This large FileMaker Pro 3.0/4.0 database has been compressed with StuffIt. The Dictionary of the Mouse Genome contains data from the 1997-98 chromosome committee reports and MIT Whitehead SSLP databases (Release 15). The Dictionary contains information for 21,039 loci. File size = 4846 KB. Updated March 19, 1998. * MIT Microsatellite Database ONLINE: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. ONLINE. Updated July 12, 2001. * MIT Microsatellite Database: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. File size = 3.0 MB. Updated March 19, 1998. * OurPrimersDB: A small database of primers. Download this database if you are using numerous MIT primers to map genes in mice. This database should be used in combination with the MITDB as one part of a relational database. File size = 149 KB. Updated March 19, 1998. * Empty copy (clone) of the Portable Dictionary in FileMaker Pro 3.0 format. Download this file and import individual chromosome text files from the table into the database. File size = 231 KB. Updated March 19, 1998. * Chromosome Text Files from the Dictionary: The table lists data on gene loci for individual chromosomes.

Proper citation: Mouse Genome Databases (RRID:SCR_007147) Copy   

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