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http://jaspar.genereg.net

Open source database of curated, non-redundant set of profiles derived from published collections of experimentally defined transcription factor binding sites for multicellular eukaryotes. Consists of open data access, non-redundancy and quality. JASPAR CORE is smaller set that is non-redundant and curated. Collection of transcription factor DNA-binding preferences, modeled as matrices. These can be converted into Position Weight Matrices (PWMs or PSSMs), used for scanning genomic sequences. Web interface for browsing, searching and subset selection, online sequence analysis utility and suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval.

Proper citation: JASPAR (RRID:SCR_003030) Copy   

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http://courses.jax.org/2012/addiction.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.

Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy   

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http://www.genomeweb.com/

An independent, privately-held online and print publisher based in New York that serves the global community of scientists, technology professionals, and executives who use and develop the latest advanced tools in molecular biology research and molecular diagnostics. GenomeWeb's editorial mission is to serve readers with exclusive, in-depth coverage of the technology, institutions, and scientists that make up the worldwide research enterprise of molecular biology. We operate the largest online news organization focused on advanced research tools in genomics, proteomics, and bioinformatics. Our expert editors report and write with precision and clarity. GenomeWeb users can be found in major scientific organizations around the world, including biopharmaceutical companies, important research universities, biomedical institutes, and government laboratories. Our advertisers include leading suppliers of research tools, analytical instruments, and information technology. Getting started is easy - just register, and use your workplace e-mail address to maximize your access to content. Once you're logged in, you'll have complete free access to GenomeWeb Daily News, The Daily Scan, all of Genome Technology magazine, every GenomeWeb blog, and much more. GenomeWeb Free Content * GenomeWeb Daily News offers breaking news as well as feature articles on genomics, proteomics, bioinformatics, and more. Daily News covers not only the science and business news, but also regulatory and policy updates. Published online and twice daily by e-mail bulletin. * The Daily Scan is a roundup of the most interesting mainstream media articles, blog posts, and peer-reviewed literature relevant to genomic and proteomic scientists. Published daily online and by e-mail bulletin. * Genome Technology: GenomeWeb's magazine covers news, trends, people, and technologies in the systems biology field. It also includes Tech Guides, which feature expert troubleshooting advice on specific lab challenges, and Research Trend Digests. Published 10 times per year. Subscriptions to the print edition are free to active researchers in the US and $29 per year for non-scientists or anyone outside the US. Non-US researchers are eligible for a free subscription to the digital edition of Genome Technology. We may contact subscribers from time to time to requalify for the magazine, in compliance with our third-party readership audit. * Careers: Our Careers page includes content to help scientists in their jobs, with links to relevant external blog posts, profiles of alternative job paths, and more. Careers also includes our Job Listings board, where anyone can post job ads for free. * Cancer Minute: Updated daily and published by e-mail bulletin weekly, Cancer Minute rounds up the latest oncology peer-reviewed literature as well as news and blog posts. * Informatics Iron: This blog covers high-performance computing and the hardware side of bioinformatics, from GPUs to compute clusters and more. * The Sample: This blog focuses on a range of topics of interest to clinical labs, including the adoption of molecular tools, issues related to lab management, in-depth coverage of the major reference labs, and more. GenomeWeb Premium Content All GenomeWeb premium content provides readers with in-depth, exclusive coverage in key technology or application areas. These publications include business, technology, and research news; patent and IP information; product launches and upgrades; and hirings, promotions, and other people news.

Proper citation: GenomeWeb (RRID:SCR_000650) Copy   

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https://monarchinitiative.org/

Repository of information about model organisms, in vitro models, genes, pathways, gene expression, protein and genetic interactions, orthology, disease, phenotypes, publications, and authors, and ability to navigate multi-scale spatial and temporal phenotypes across in vivo and in vitro model systems in context of genetic and genomic data, using semantics and statistics. Discovery system provides basic and clinical science researchers, informaticists, and medical professionals with integrated interface and set of discovery tools to reveal genetic basis of disease, facilitate hypothesis generation, and identify novel candidate drug targets. Database that indexes authoritative information on experimental models of disease from MGI, RGD and ZFIN.

Proper citation: MONARCH Initiative (RRID:SCR_000824) Copy   

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http://mlemire.freeshell.org/SimM.README

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 6th,2023. Gene dropping simulation software. The program is a gzip'ed tar archive and is designed to run under UNIX/Linux operating system.

Proper citation: SIMM (RRID:SCR_000849) Copy   

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http://csg.sph.umich.edu//abecasis/MACH/index.html

A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.

Proper citation: MACH 1.0 (RRID:SCR_001759) Copy   

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http://mousecyc.jax.org/

A manually curated database of both known and predicted metabolic pathways for the laboratory mouse. It has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human. The database records for 1,060 genes in Mouse Genome Informatics (MGI) are linked directly to 294 pathways with 1,790 compounds and 1,122 enzymatic reactions in MouseCyc. (Aug. 2013) BLAST and other tools are available. The initial focus for the development of MouseCyc is on metabolism and includes such cell level processes as biosynthesis, degradation, energy production, and detoxification. MouseCyc differs from existing pathway databases and software tools because of the extent to which the pathway information in MouseCyc is integrated with the wealth of biological knowledge for the laboratory mouse that is available from the Mouse Genome Informatics (MGI) database.

Proper citation: MouseCyc (RRID:SCR_001791) Copy   

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https://rgd.mcw.edu/rgdweb/portal/home.jsp?p=4

An integrated resource for information on genes, QTLs and strains associated with diabetes. The portal provides easy acces to data related to both Type 1 and Type 2 Diabetes and Diabetes-related Obesity and Hypertension, as well as information on Diabetic Complications. View the results for all the included diabetes-related disease states or choose a disease category to get a pull-down list of diseases. A single click on a disease will provide a list of related genes, QTLs, and strains as well as a genome wide view of these via the GViewer tool. A link from GViewer to GBrowse shows the genes and QTLs within their genomic context. Additional pages for Phenotypes, Pathways and Biological Processes provide one-click access to data related to diabetes. Tools, Related Links and Rat Strain Models pages link to additional resources of interest to diabetes researchers.

Proper citation: Diabetes Disease Portal (RRID:SCR_001660) Copy   

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http://www.comp-sys-bio.org/yeastnet/

This is a portal to the consensus yeast metabolic network as reconstructed from the genome sequence and literature. It is a highly annotated metabolic map that is periodically updated by a team of collaborators from various research groups. The first version of this reconstruction was published in Herrgrd, Swainston et al. (2008) A consensus yeast metabolic reconstruction obtained from a community approach to systems biology Nature Biotechnol. 26, 1155-1160 (you can access that network here). A second version has now been released and is awaiting publication. We plan on continuing to update this resource towards a complete metabolic network of yeast. All versions will remain accessible for historical purposes, however it is highly recommended that you always use the latest one since that is the most up to date. This effort started on the shoulders of a number of reconstructions of the metabolic network of yeast based on genomic and literature data that were published separately. (iMM904 and iLL672) However, due to the different approaches utilized in them, those earlier reconstructions had a significant number of differences. In addition they suffered from the use of non-standard names and overall they were not annotated with methods that are machine-readable. A community effort in 2007, led by the Manchester Centre for Integrative Systems Biology and the YSBN resulted in a consensus network representation of yeast metabolism, reconciling the earlier results. That effort is now ongoing under the leadership of the MCISB and with collaboration with colleagues under the UNICELLSYS FP7 project. Availability The network reconstruction is primarily assembled and provided as an SBML file enriched with MIRIAM-compliant annotations (which are embedded in the SBML through RDF). All small and macro- molecules are referenced to an authoritative database (e.g. Uniprot, ChEBI, etc.). All molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Thus this network is entirely traceable and is presented in a computational framework. SBML is a format that is understood by a large number of software applications (see sbml.org). While the SBML file is the most efficient computational resource for these data, casual users also need access to the network. That is provided by a searchable relational database accessed directly from this website. The database pages also allow readers to add comments to any chemical species or reaction. Such comments are taken into consideration by the team collating new versions of the network and can lead to corrections and additions to the network. This reconstruction is provided in the following formats: :* an SBML file containing the reaction network and annotations, located to specific sub-cellular compartments :* an SBML file containing the reaction network and annotations without subcellular compartmentation (all reactions happening in a single compartment). :* a searcheable relational database, which uses the B-Net software from Pedro Mendes' group. The database version of this data set is managed with the B-Net software created in Pedro Mendes' group at the Virginia Bioinformatics Institute. B-Net's schema is a detailed representation of the underlying biochemistry and regulation. A number of reconstructions of the metabolic network of yeast based on genomic and literature data have been published. However, due to different approaches utilized in the reconstruction as well as different interpretations of the literature, the earlier reconstructions have significant number of differences. A community effort resulted in a consensus network model of yeast metabolism, combining results from previous models.

Proper citation: Yeast consensus metabolic network - A consensus reconstruction of yeast metabolism (RRID:SCR_002135) Copy   

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http://www.broadinstitute.org/mpg/snap/

A computer program and web-based service for the rapid retrieval of linkage disequilibrium proxy single nucleotide polymorphism (SNP) results given input of one or more query SNPs and based on empirical observations from the International HapMap Project and the 1000 Genomes Project. A series of filters allow users to optionally retrieve results that are limited to specific combinations of genotyping platforms, above specified pairwise r2 thresholds, or up to a maximum distance between query and proxy SNPs. SNAP can also generate linkage disequilibrium plots

Proper citation: SNAP - SNP Annotation and Proxy Search (RRID:SCR_002127) Copy   

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https://software.broadinstitute.org/gatk/

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

Proper citation: GATK (RRID:SCR_001876) Copy   

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http://wwwn.cdc.gov/dls/genetics/rmmaterials/default.aspx

The goal of the Genetic Testing Reference Materials Coordination Program (GeT-RM) is to coordinate a self-sustaining community process to improve the availability of appropriate and characterized reference materials for: Quality control (QC), Proficiency testing (PT), Test development & validation, Research. The purpose of this program is: - To help the genetic testing community obtain appropriate and characterized reference materials - To facilitate and coordinate information exchange between users and providers of QC and reference materials - To coordinate efforts for contribution, development, characterization and distribution of reference materials for genetic testing Get-RM provides information about cell lines, DNA, and other kinds of materials that could be used as reference materials for molecular genetic testing. Some of these materials have been characterized by the GeT-RM program and can be divided into three categories: - Genetic Inherited Disease & Pharmacogenetics This section includes information about cell lines, DNA, and other samples that can be used as reference materials for various inherited diseases (including cystic fibrosis, fragile X, Huntington disease, and Ashkenazi Jewish-related diseases), pharmacogenetic loci, and biochemical genetics. The GeT-RM program has confirmed the genotype of many of the genomic DNA samples through testing in multiple clinical genetic laboratories. - Molecular Oncology This section includes information about commercially available cell lines, DNA, and other kinds of materials that could be used as reference materials for various types of cancers, including leukemia/lymphoma and solid tumors. - Infectious Disease This section includes information about commercially available cell lines, DNA, and other kinds of materials that could be used as reference materials for various infectious disease pathogens including viruses, bacteria, and protozoa.

Proper citation: Center for Disease Control and Prevention: Genetic Testing Reference Materials Coordination Program (RRID:SCR_013029) Copy   

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http://sift.bii.a-star.edu.sg/

Data analysis service to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations. (entry from Genetic Analysis Software) Web service is also available.

Proper citation: SIFT (RRID:SCR_012813) Copy   

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http://web.bioinformatics.ic.ac.uk/eqtlexplorer/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 1,2023. eQTL Explorer was developed as a computational resource to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. This visualization tool stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer also provides a new and powerful tool to interrogate these very large and complex datasets. eQTLexplorer allows users to mine and understand data from a repository of genetical genomics experiments. It will graphically display eQTL information based on a certain number of selection criteria, including: tissue type, p-value, cis/trans, probeset Affymetrix id and PQTL type. Sponsors: This work was funded by the MRC Clinical Sciences Centre and the Wellcome Trust programme for Cardiovascular Functional Genomics.

Proper citation: eQTL Visualization Tool (RRID:SCR_013413) Copy   

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http://www.jurgott.org/linkage/ListSoftware.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Listing of computer software for the gene mapping community on the following topics: genetic linkage analysis for human pedigree data, QTL analysis for animal/plant breeding data, genetic marker ordering, genetic association analysis, haplotype construction, pedigree drawing, and population genetics. The inclusion of a program should not be interpreted as an endorsement to that program from us. In the last few years, new technology produces new types of genetic data, and the scope of genetic analyses change dramatically. It is no longer obvious whether a program should be included or excluded from this list. Topics such as next-generation-sequencing (NGS), gene expression, genomics annotation, etc. can all be relevant to a genetic study, yet be specialized topics by themselves. Though programs on variance calling from NSG can be in, those can sequence alignment might be out; programs on eQTL can be in, those on differential expression might be out. This page was created by Dr. Wentian Li, when he was at Columbia University (1995-1996). It was later moved to Rockefeller University (1996-2002), and now takes its new home at North Shore LIJ Research Institute (2002-now). The present copy is maintained by Jurg Ott as a single file. More than 240 programs have been listed by December 2004, more than 350 programs by August 2005, close to 400 programs by December 2006, and close to 480 programs by November 2008, and over 600 programs by October 2012. A version of the searchable database was developed by Zhiliang Hu of Iowa State University, and a recent round of updating was assisted by Wei JIANG of Harbin Medical School. Some earlier software can be downloaded from EBI: ftp://ftp.ebi.ac.uk/pub/software/linkage_and_mapping/ (Linkage and Mapping Software Repository), and http://genamics.com/software/index.htm may contain archived copy of some programs.

Proper citation: Genetic Analysis Software (RRID:SCR_013155) Copy   

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http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software application that simultaneously estimates a trait-locus position and its genetic effects for affected relative pairs (ARP) by one of two methods. Either allow a different trait-locus effect for each ARP type, or constrain the trait-locus effects according to the marginal effect of a single susceptibility locus. We include a goodness of fit statistic for the constrained model. (entry from Genetic Analysis Software)

Proper citation: ARP.GEE (RRID:SCR_013134) Copy   

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https://doi.org/10.1016/j.genrep.2019.100414

Eutherian comparative genomic analysis protocol as one framework of eutherian gene data set revisions. Protocol integrated gene annotations, phylogenetic analysis and protein molecular evolution analysis with 3 new tests including test of reliability of public eutherian genomic sequences using genomic sequence redundancies, test of contiguity of public eutherian genomic sequences using multiple pairwise genomic sequence alignments and test of protein molecular evolution using relative synonymous codon usage statistics. Public eutherian reference genomic sequence data sets.

Proper citation: Eutherian comparative genomic analysis protocol (RRID:SCR_014401) Copy   

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http://bar.utoronto.ca/welcome.htm

Web-based tools for working with functional genomics and other data, including Gene Expression and Protein Tools, Molecular Markers and Mapping Tools, and Other Genomic Tools. Most are designed with the plant (mainly Arabidopsis) researcher in mind, but a couple of them can be useful to the wider research community, e.g. Mouse eFP Browser or BlastDigester. The associated paper for most tools is available.

Proper citation: BAR (RRID:SCR_006748) Copy   

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http://humancyc.org/

The HumanCyc database describes human metabolic pathways and the human genome. By presenting metabolic pathways as an organizing framework for the human genome, HumanCyc provides the user with an extended dimension for functional analysis of Homo sapiens at the genomic level. A computational pathway analysis of the human genome assigned human enzymes to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary step toward quantitative modeling of metabolism. HumanCyc contains the complete genome sequence of Homo sapiens, as presented in Build 31. Data on the human genome from Ensembl, LocusLink and GenBank were carefully merged to create a minimally redundant human gene set to serve as an input to SRI''s PathoLogic software, which generated the database and predicted Homo sapiens metabolic pathways from functional information contained in the genome''s annotation. SRI did not re-annotate the genome, but worked with the gene function assignments in Ensembl, LocusLink, and GenBank. The resulting pathway/genome database (PGDB) includes information on 28,783 genes, their products and the metabolic reactions and pathways they catalyze. Also included are many links to other databases and publications. The Pathway Tools software/database bundle includes HumanCyc and the Pathway Tools software suite and is available under license. This form of HumanCyc is faster and more powerful than the Web version.

Proper citation: HumanCyc: Encyclopedia of Homo sapiens Genes and Metabolism (RRID:SCR_007050) Copy   

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http://goblet.molgen.mpg.de/cgi-bin/goblet2008/goblet.cgi

Tool that performs annotation based on GO and pathway terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. GOblet expects query sequences to be in FASTA-Format (with header-lines). Protein and nucleotide sequences are accepted. Total size of all sequences submitted per request should not be larger than 50kb currently. For security reasons: Larger post's will be rejected. Due to limited capacities the queries may be processed in batches depending on the server load. The output of the BLAST job is filtered automatically and the relevant hits are displayed. In addition, the respective GO-terms are shown together with the complete GO-hierarchy of parent terms., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOblet (RRID:SCR_006998) Copy   

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