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http://genecodis.cnb.csic.es/

Web-based tool for the ontological analysis of large lists of genes. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example Biological Process and Cellular Component categories of Gene Ontology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneCodis (RRID:SCR_006943) Copy   

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http://www.ncbi.nlm.nih.gov/clinvar/

Archive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.

Proper citation: ClinVar (RRID:SCR_006169) Copy   

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http://www.oeb.harvard.edu/faculty/hartl/old_site/lab/publications/GeneMerge.html

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Web-based and standalone application that returns a wide range of functional genomic data for a given set of study genes and provides rank scores for over-representation of particular functions or categories in the data. It uses the hypergeometric test statistic which returns statistically correct results for samples of all sizes and is the #2 fastest GO tool available (Khatri and Draghici, 2005). GeneMerge can be used with any discrete, locus-based annotation data, including, literature references, genetic interactions, mutant phenotypes as well as traditional Gene Ontology queries. GeneMerge is particularly useful for the analysis of microarray data and other large biological datasets. The big advantage of GeneMerge over other similar programs is that you are not limited to analyzing your data from the perspective of a pre-packaged set of gene-association data. You can download or create gene-association files to analyze your data from an unlimited number of perspectives. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMerge (RRID:SCR_005744) Copy   

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http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

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http://neibank.nei.nih.gov

An integrated resource for genomics and bioinformatics in vision research including expressed sequence tag (EST) data and sequence-verified cDNA clones for multiple eye tissues of several species, web-based access to human eye-specific SAGE data through EyeSAGE, and comprehensive, annotated databases of known human eye disease genes and candidate disease gene loci. All expression- and disease-related data are integrated in EyeBrowse, an eye-centric genome browser. NEIBank provides a comprehensive overview of current knowledge of the transcriptional repertoires of eye tissues and their relation to pathology. The data can be interrogated in several ways. Specific gene names can be entered into the search window. Alternatively, regions of the genome can be displayed. For example, entering two STS markers separated by a semicolon (e.g. RH18061;RH80175) allows the display of the entire chromosomal region associated with the mapping of a specific disease locus. ESTs for each tissue can then be displayed to help in the selection of candidate genes. In addition, sequences can be entered into a BLAST search and rapidly aligned on the genome, again showing eye derived ESTs for the same region. To see the same region at the full UCSC site, cut and paste the location from the position window of the genome browser. EyeBrowse includes a custom track display SAGE data for human eye tissues derived from the EyeSAGE project. The track shows the normalized sum of SAGE tag counts from all published eye-related SAGE datasets centered on the position of each identifiable Unigene cluster. This indicates relative activity of each gene locus in eye. Clicking on the vertical count bar for a particular location will bring up a display listing gene details and linking to specific SAGE counts for each eye SAGE library and comparisons with normalized sums for neural and non-neural tissues. To view or alter settings for the EyeSAGE track on EyeBrowse, click on the vertical gray bar at the left of the display. Other custom tracks display known eye disease genes and mapped intervals for candidate loci for retinal disease, cataract, myopia and cornea disease. These link back to further information at NEIBank.

Proper citation: NEIBank (RRID:SCR_007294) Copy   

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https://sourceforge.net/projects/fraggenescan/

A software application for finding fragmented genes in short reads and may be applied to predict prokaryotic genes in incomplete assemblies or complete genomes.

Proper citation: FragGeneScan (RRID:SCR_011929) Copy   

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http://ecoliwiki.net/colipedia/index.php/Welcome_to_EcoliWiki

A component of EcoliHub, EcoliWiki is a wiki-based system for finding, editing, and adding information about E. coli K-12 and other model organism strains of E. coli. EcoliWiki is being constructed to include information about bacteriophage, plasmids, and mobile genetic elements. Information should be easily accessible and correct, and users have the right to edit any information they feel is incorrect. Most of the E. coli information was initially seeded with a subset of information from parsing EcoCyc data dumps. For phage gamma and the F plasmid, Genbank accessions were converted to GFF, which was parsed into the appropriate tables. Other sources of content include: * user additions * monthly addition of annotations from EcoCyc * structural data from the PDB * domains and motif information from InterPro * various databases including EcoGene, RegulonDB, Genbank, GenoBase, ASAP * many many scientific papers EcoliWiki participates in the RefGenome project. EcoliWiki provides REST web services as part of the EcoliHub Web Services infrastructure project.

Proper citation: EcoliWiki (RRID:SCR_010656) Copy   

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http://genes.toronto.edu/

The Frey Lab develops techniques that use large scale datasets to derive predictive models of how genes and many other genomic features act in combination to produce genetic messages that control cellular activities. We have most recently focused on how organisms use alternative splicing to generate a tremendous level of biological complexity that cannot be explained by gene expression alone (Nature, 2010). Some of the tools, software and databases provided by the Frey Lab are affinity propagation, splicing prediction, PTMClust - A Post-translational Modification Refinement Algorithm, the ''epitome'': A new model of patterns, transformation invariant clustering and subspaces, learning flexible sprites from images and videos, phase unwrapping by loopy belief propagation, useful Matlab scripts, bioinformatics links, and SeedSearcher: A motif finder.

Proper citation: Frey Lab (RRID:SCR_008859) Copy   

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http://www.cbs.dtu.dk/services/HMMgene/

Data analysis service for prediction of vertebrate and C. elegans genes.

Proper citation: HMMgene (RRID:SCR_011933) Copy   

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http://cgap.nci.nih.gov/Chromosomes/Mitelman

The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer. The data in the Mitelman Database is manually culled from the literature and subsequently organized into three distinct sub-databases, as follows: -The sub-database of cases contains the data that relates chromosomal aberrations to specific tumor characteristics in individual patient cases. It can be searched using either the Cases Quick Searcher or the Cases Full Searcher. -The sub-database of molecular biology and clinical associations contains no data from individual patient cases. Instead, the data is pulled from studies with distinct information about: -Molecular biology associations that relate chromosomal aberrations and tumor histologies to genomic sequence data, typically genes rearranged as a consequence of structural chromosome changes. -Clinical associations that relate chromosomal aberrations and/or gene rearrangements and tumor histologies to clinical variables, such as prognosis, tumor grade, and patient characteristics. It can be searched using the Molecular Biology and Clinical (MBC) Associations Searcher -The reference sub-database contains all the references culled from the literature i.e., the sum of the references from the cases and the molecular biology and clinical associations. It can be searched using the Reference Searcher. CGAP has developed six web search tools to help you analyze the information within the Mitelman Database: -The Cases Quick Searcher allows you to query the individual patient cases using the four major fields: aberration, breakpoint, morphology, and topography. -The Cases Full Searcher permits a more detailed search of the same individual patient cases as above, by including more cytogenetic field choices and adding search fields for patient characteristics and references. -The Molecular Biology Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to gene rearrangements as a consequence of cytogenetic aberrations. -The Clinical Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to clinical associations of cytogenetic aberrations and/or gene rearrangements. -The Recurrent Chromosome Aberrations Searcher provides a way to search for structural and numerical abnormalities that are recurrent, i.e., present in two or more cases with the same morphology and topography. -The Reference Searcher queries only the references themselves, i.e., the references from the individual cases and the molecular biology and clinical associations. Sponsors: This database is sponsored by the University of Lund, Sweden and have support from the Swedish Cancer Society and the Swedish Children''s Cancer Foundation

Proper citation: Mitelman Database of Chromosome Aberrations in Cancer (RRID:SCR_012877) Copy   

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http://biome.ewha.ac.kr:8080/GSEAWebApp/

An integrative platform for diverse types of gene set analysis with annotation network navigation. It includes tools for statistical analysis, visualization of annotation relationships, retrieval of genes from annotation database, and set operation for gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, they have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations.

Proper citation: GARNET (RRID:SCR_012033) Copy   

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http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

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http://geneticassociationdb.nih.gov/

The Genetic Association Database is an archive of human genetic association studies of complex diseases and disorders. The goal of this database is to allow the user to rapidly identify medically relevant polymorphism from the large volume of polymorphism and mutational data, in the context of standardized nomenclature. The data is from published scientific papers. Study data is recorded in the context of official human gene nomenclature with additional molecular reference numbers and links. It is gene centered. That is, each record is a record of a gene or marker. If a study investigated 6 genes for a particular disorder, there will be 6 records. Anyone may view this database and anyone may submit records. You do not have to be an author on the original study to submit a record. All submitted records will be reviewed before inclusion in the archive. Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. From a comprehensive search of the MeSH annotation of MEDLINE articles, they identified 3,342 environmental etiological factors associated with 3,159 diseases. They also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the etiome, which they define as the comprehensive compendium of disease etiology.

Proper citation: Genetic Association Database (RRID:SCR_013264) Copy   

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http://sbrg2.gist.ac.kr/hcnet/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A specialized database for mouse heart and calcium signaling toolkit genes. It contains the functional gene modules pre-calculated from the microarray data compendium using various algorithms for genetic network analyses. The Heart and Calcium functional Network (HCNet) database is a collection of functional gene clusters calculated from microarray data compendium obtained from the Korea Systems Biology Initiative and from the publicly available GEO database. It was designed to assist experimentalists especially in the field of cardiac and calcium signaling research to detect potential network motifs and gene clusters that are functionally related or co-regulated by common transcription factors. Genes of defined numbers are classified into two categories, 1) heart-specific genes and 2) heart-specific genes plus calcium signaling toolkit-genes.

Proper citation: Heart and Calcium Functional Network Database (RRID:SCR_013515) Copy   

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http://xin.cz3.nus.edu.sg/group/trmp/trmp.asp

The Therapeutically Relevant Multiple Pathways Database is designed to provide information about such multiple pathways and related therapeutic targets described in the literatures, the targeted disease conditions, and the corresponding drugs/ligands directed at each of these targets. This database currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions along with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. Additional information provided include protein name, synonyms, Swissprot AC number, species, gene name and location, protein sequence (AASEQ) and gene sequence (NTSEQ) as well as potential therapeutic implications while applicable. Cross-links to other databases are provided which include Genecard, GDB, Locuslink, NCBI, KEGG, OMIM, SwissProt to facilitate the access of more detailed information about various aspects of the particular target or non-target protein. Queries can be submitted by entering or selecting the required information in any one or combination of the fields in the form. User can specify full name or any part of the name in a text field, or choose one item from an selection field. Sponsors: TRMP is supported by the National University of Singapore.

Proper citation: Therapeutically Relevant Multiple Pathways Database (RRID:SCR_013471) Copy   

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http://signal.salk.edu/cgi-bin/RiceGE

Gene database for Japonica rice. RiceGE is associated with SIGnAL at the Salk Institute.

Proper citation: RiceGE (RRID:SCR_015061) Copy   

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http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

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https://ckb.jax.org/

Semi-automated and manually curated database of gene/variant annotations, therapy knowledge, diagnostic/prognostic information, and oncology clinical trials. Users can search CKB via gene, gene variants, drug, drug class, indication, and clinical trials.

Proper citation: Jackson Laboratory Clinical Knowledgebase (RRID:SCR_014965) Copy   

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http://www.informatics.jax.org/humanDisease.shtml

Collection of published and potential mouse models of human disease, discovery of candidate genes and investigation of phenotypic similarity between mouse models and human patients. Mouse mutation, and phenotype and disease model data from Mouse Genome Informatics database are integrated with human gene to disease relationships from the National Center for Biotechnology Information and Online Mendelian Inheritance in Man and human disease to phenotype relationships from the Human Phenotype Ontology.

Proper citation: Human Mouse Disease Connection (RRID:SCR_017522) Copy   

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http://www.gcbi.com.cn

Database provides information on research status of gene related diseases, gene expression in different tissues, regulatory relationship among genes, information of diseases, mRNA, Transcription Fctor, miRNA and other information shared among genes.

Proper citation: GCBI database (RRID:SCR_018971) Copy   

•   Source: SciCrunch Registry