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http://psychiatry.igm.jhmi.edu/SynaptomeDB/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Ontology-based knowledgebase for synaptic genes. These genes encode components of the synapse including neurotransmitters and their receptors, adhesion / cytoskeletal proteins, scaffold proteins, transporters, and others. It integrates various and complex data sources for synaptic genes and proteins., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SynaptomeDB (RRID:SCR_000157) Copy   

•   Source: SciCrunch Registry

http://mitominer.mrc-mbu.cam.ac.uk/

A database of mitochondrial proteomics data. It includes two sets of proteins: the MitoMiner Reference Set, which has 10477 proteins from 12 species; and MitoCarta, which has 2909 proteins from mouse and human mitochondrial proteins. MitoMiner provides annotation from the Gene Ontology (GO) and UniProt databases. This reference set contains all proteins that are annotated by either of these resources as mitochondrial in any of the species included in MitoMiner. MitoMiner data via is available via Application Programming Interface (API). The client libraries are provided in Perl, Python, Ruby and Java.

Proper citation: MitoMiner (RRID:SCR_001368) Copy   

•   Source: SciCrunch Registry

http://www.yeastract.com

A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database.

Proper citation: Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) Copy   

•   Source: SciCrunch Registry

http://malacards.org

An integrated database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. The database contains 17,705 diseases, consolidated from 28 sources.

Proper citation: MalaCards (RRID:SCR_005817) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry

http://pbildb1.univ-lyon1.fr/virhostnet/

Public knowledge base specialized in the management and analysis of integrated virus-virus, virus-host and host-host interaction networks coupled to their functional annotations. It contains high quality and up-to-date information gathered and curated from public databases (VirusMint, Intact, HIV-1 database). It allows users to search by host gene, host/viral protein, gene ontology function, KEGG pathway, Interpro domain, and publication information. It also allows users to browse viral taxonomy.

Proper citation: VirHostNet: Virus-Host Network (RRID:SCR_005978) Copy   

•   Source: SciCrunch Registry

http://www.hprd.org

Database that represents a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome. All the information in HPRD has been manually extracted from the literature by expert biologists who read, interpret and analyze the published data.

Proper citation: HPRD - Human Protein Reference Database (RRID:SCR_007027) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?q=*&t=indexable&list=cover&nif=nlx_154697-2

A virtual database of annotations made by 50 database providers (April 2014) - and growing (see below), that map data to publication information. All NIF Data Federation sources can be part of this virtual database as long as they indicate the publications that correspond to data records. The format that NIF accepts is the PubMed Identifier, category or type of data that is being linked to, and a data record identifier. A subset of this data is passed to NCBI, as LinkOuts (links at the bottom of PubMed abstracts), however due to NCBI policies the full data records are not currently associated with PubMed records. Database providers can use this mechanism to link to other NCBI databases including gene and protein, however these are not included in the current data set at this time. (To view databases available for linking see, http://www.ncbi.nlm.nih.gov/books/NBK3807/#files.Databases_Available_for_Linking ) The categories that NIF uses have been standardized to the following types: * Resource: Registry * Resource: Software * Reagent: Plasmid * Reagent: Antibodies * Data: Clinical Trials * Data: Gene Expression * Data: Drugs * Data: Taxonomy * Data: Images * Data: Animal Model * Data: Microarray * Data: Brain connectivity * Data: Volumetric observation * Data: Value observation * Data: Activation Foci * Data: Neuronal properties * Data: Neuronal reconstruction * Data: Chemosensory receptor * Data: Electrophysiology * Data: Computational model * Data: Brain anatomy * Data: Gene annotation * Data: Disease annotation * Data: Cell Model * Data: Chemical * Data: Pathways For more information refer to Create a LinkOut file, http://neuinfo.org/nif_components/disco/interoperation.shtm Participating resources ( http://disco.neuinfo.org/webportal/discoLinkoutServiceSummary.do?id=4 ): * Addgene http://www.addgene.org/pgvec1 * Animal Imaging Database http://aidb.crbs.ucsd.edu * Antibody Registry http://www.neuinfo.org/products/antibodyregistry/ * Avian Brain Circuitry Database http://www.behav.org/abcd/abcd.php * BAMS Connectivity http://brancusi.usc.edu/ * Beta Cell Biology Consortium http://www.betacell.org/ * bioDBcore http://biodbcore.org/ * BioGRID http://thebiogrid.org/ * BioNumbers http://bionumbers.hms.harvard.edu/ * Brain Architecture Management System http://brancusi.usc.edu/bkms/ * Brede Database http://hendrix.imm.dtu.dk/services/jerne/brede/ * Cell Centered Database http://ccdb.ucsd.edu * CellML Model Repository http://www.cellml.org/models * CHEBI http://www.ebi.ac.uk/chebi/ * Clinical Trials Network (CTN) Data Share http://www.ctndatashare.org/ * Comparative Toxicogenomics Database http://ctdbase.org/ * Coriell Cell Repositories http://ccr.coriell.org/ * CRCNS - Collaborative Research in Computational Neuroscience - Data sharing http://crcns.org * Drug Related Gene Database https://confluence.crbs.ucsd.edu/display/NIF/DRG * DrugBank http://www.drugbank.ca/ * FLYBASE http://flybase.org/ * Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/ * Gene Ontology Tools http://www.geneontology.org/GO.tools.shtml * Gene Weaver http://www.GeneWeaver.org * GeneDB http://www.genedb.org/Homepage * Glomerular Activity Response Archive http://gara.bio.uci.edu * GO http://www.geneontology.org/ * Internet Brain Volume Database http://www.cma.mgh.harvard.edu/ibvd/ * ModelDB http://senselab.med.yale.edu/modeldb/ * Mouse Genome Informatics Transgenes ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt * NCBI Taxonomy Browser http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html * NeuroMorpho.Org http://neuromorpho.org/neuroMorpho * NeuronDB http://senselab.med.yale.edu/neurondb * SciCrunch Registry http://neuinfo.org/nif/nifgwt.html?tab=registry * NIF Registry Automated Crawl Data http://lucene1.neuinfo.org/nif_resource/current/ * NITRC http://www.nitrc.org/ * Nuclear Receptor Signaling Atlas http://www.nursa.org * Olfactory Receptor DataBase http://senselab.med.yale.edu/ordb/ * OMIM http://omim.org * OpenfMRI http://openfmri.org * PeptideAtlas http://www.peptideatlas.org * RGD http://rgd.mcw.edu * SFARI Gene: AutDB https://gene.sfari.org/autdb/Welcome.do * SumsDB http://sumsdb.wustl.edu/sums/ * Temporal-Lobe: Hippocampal - Parahippocampal Neuroanatomy of the Rat http://www.temporal-lobe.com/ * The Cell: An Image Library http://www.cellimagelibrary.org/ * Visiome Platform http://platform.visiome.neuroinf.jp/ * WormBase http://www.wormbase.org * YPED http://medicine.yale.edu/keck/nida/yped.aspx * ZFIN http://zfin.org

Proper citation: Integrated Manually Extracted Annotation (RRID:SCR_008876) Copy   

•   Source: SciCrunch Registry

https://www.swisslipids.org/

Expert curated resource that provides framework for integration of lipid and lipidomic data with biological knowledge and models. Provides curated knowledge of lipid structures and metabolism which is used to generate in silico library of feasible lipid structures. These are arranged in hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. Provides reference namespace for lipidomic data publication, data exploration and hypothesis generation.

Proper citation: SwissLipids (RRID:SCR_019074) Copy   

•   Source: SciCrunch Registry

http://www.lipidmaps.org/data/proteome/LMPD.php

Database of lipid related proteins representing human and mouse proteins involved in lipid metabolism. Collection of lipid related genes and proteins contains data for genes and proteins from Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae, Caenorhabditis elegans, Escherichia coli, Macaca mulata, Drosophila melanogaster, Arabidopsis thaliana and Danio rerio.

Proper citation: LIPID MAPS Proteome Database (RRID:SCR_003062) Copy   

•   Source: SciCrunch Registry

http://integrativebiology.org/

Database for molecular interaction information integrated with various other bio-entity information, including pathways, diseases, gene ontology (GO) terms, species and molecular types. The information is obtained from several manually curated databases and automatic extraction from literature. There are protein-protein interaction, gene/protein regulation and protein-small molecule interaction information stored in the database. The interaction information is linked with relevant GO terms, pathway, disease and species names. Interactions are also linked to the PubMed IDs of the corresponding abstracts the interactions were obtained from. Manually curated molecular interaction information was obtained from BioGRID, IntAct, NCBI Gene, and STITCH database. Pathway related information was obtained from KEGG database, Pathway Interaction database and Reactome. Disease information was obtained from PharmGKB and KEGG database. Gene ontology terms and related information was obtained from Gene Ontology database and GOA database.

Proper citation: Integrated Molecular Interaction Database (RRID:SCR_003546) Copy   

•   Source: SciCrunch Registry

http://150.216.56.64/index.php

Database platform for cotton expressed sequence tag (EST)-related information, covering assembled contigs, function annotation, analysis of GO and KEGG, SNP, miRNA, SSR-related marker information.

Proper citation: Cotton EST Database (RRID:SCR_003301) Copy   

•   Source: SciCrunch Registry

http://oboedit.org/

OBO-Edit is an open source, platform-independent application written in Java for viewing and editing any OBO format ontologies. OBO-Edit is a graph-based tool; its emphasis on the overall graph structure of an ontology provides a friendly interface for biologists, and makes OBO-Edit excellent for the rapid generation of large ontologies focusing on relationships between relatively simple classes. The UI components are cleanly separated from the data model and data adapters, so these can be reused in other applications. The oboedit foward-chaining reasoner can also be used independently (for example, for traversing ontology graphs). OBO-Edit uses the OBO format flat file. See the GO wiki, http://wiki.geneontology.org/index.php/OBO-Edit:_Getting_the_Source_Code, for instructions on downloading the source code. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OBO-Edit (RRID:SCR_005668) Copy   

•   Source: SciCrunch Registry

http://systemsbio.ucsd.edu/GoSurfer/

GoSurfer uses Gene Ontology (GO) information to analyze gene sets obtained from genome-wide computations or microarray analyses. GoSurfer is a graphical interactive data mining tool. It associates user input genes with GO terms and visualizes such GO terms as a hierarchical tree. Users can manipulate the tree output by various means, like setting heuristic thresholds or using statistical tests. Significantly important GO terms resulted from a statistical test can be highlighted. All related information are exportable either as texts or as graphics. Platform: Windows compatible

Proper citation: GoSurfer (RRID:SCR_005789) Copy   

•   Source: SciCrunch Registry

http://www.wandora.org/wandora/wiki/index.php?title=Main_Page

Wandora is a general purpose information extraction, management and publishing application based on Topic Maps and Java. Wandora has graphical user interface, layered and merging information model, multiple visualization models, huge collection of information extraction, import and export options, embedded HTTP server with several output modules and open plug-in architecture. Wandora is a FOSS application with GNU GPL license. Wandora is well suited for constructing ontologies and information mashups. Wandora is capable of extracting and converting a wide range of open data feeds to topic map formats. Beyond topic map conversion, this feature allows Wandora user to aggregate multidimensional information mashups where information from Flickr interleaves with information from GeoNames and YouTube, for example. Wandora is a software application to build, edit, publish and visualize information graphs, especially topic maps. Wandora is written in Java and suits for * Collecting, combining, aggregating, managing, refining and publishing information and knowledge graphs * Designing information, information modeling and prototyping * Information mashups * Ontology creation and management * Mind and concept mapping * Language technology applications * Graph visualizations * Knowledge format conversions * Digital preservation * Data journalism * Open data projects * Linked data projects Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Wandora (RRID:SCR_005689) Copy   

•   Source: SciCrunch Registry

http://dbserv2.informatik.uni-leipzig.de:8080/onex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Web-based application that integrates versions of 16 life science ontologies including the Gene Ontology, NCI Thesaurus and selected OBO ontologies with data leading back to 2002 in a common repository to explore ontology changes. It allows to study and apply the evolution of these integrated ontologies on three different levels. It provides global ontology evolution statistics and ontology-specific evolution trends for concepts and relationships and it allows the migration of annotations in case a new ontology version was released

Proper citation: OnEx - Ontology Evolution Explorer (RRID:SCR_000602) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/categoryCompare.html

A software package for meta-analysis of high-throughput experiments using feature annotations. It calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Proper citation: categoryCompare (RRID:SCR_001223) Copy   

•   Source: SciCrunch Registry

http://akt.ucsf.edu/EGAN/

Exploratory Gene Association Networks (EGAN) is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.). EGAN functions as a module for exploratory investigation of analysis results from multiple high-throughput assay technologies, including but not limited to: * Transcriptomics via expression microarrays or RNA-Seq * Genomics via SNP GWAS or array CGH * Proteomics via MS/MS peptide identifications * Epigenomics via DNA methylation, ChIP-on-Chip or ChIP-Seq * In-silico analysis of sequences or literature EGAN has been built using Cytoscape libraries for graph visualization and layout, and is comparable to DAVID, GSEA, Ingenuity IPA and Ariadne Pathway Studio. There are pre-collated EGAN networks available for human (Homo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), chicken (Gallus gallus), zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), nematode (Caenorhabditis elegans), mouse-ear cress (Arabidopsis thaliana), rice (Oryza sativa) and brewer's yeast (Saccharomyces cerevisiae). There is now an EGAN module available for GenePattern (human-only). Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: EGAN: Exploratory Gene Association Networks (RRID:SCR_008856) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/biomaRt/

Software package that integrates BioMart data resources with data analysis software in Bioconductor. Can annotate range of gene or gene product identifiers including Entrez Gene and Affymetrix probe identifiers with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis.

Proper citation: biomaRt (RRID:SCR_019214) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.ubc.ca/ermineJ/

Data analysis software for gene sets in expression microarray data or other genome-wide data that results in rankings of genes. A typical goal is to determine whether particular biological pathways are doing something interesting in the data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Major features include: * Implementation of multiple methods for gene set analysis: ** Over-representation analysis ** A resampling-based method that uses gene scores ** A rank-based method that uses gene scores ** A resampling-based method that uses correlation between gene expression profiles (a type of cluster-enrichment analysis). * Gene sets receive statistical scores (p-values), and multiple test correction is supported. * Support of the Gene Ontology terminology; users can choose which aspects to analyze. * User files use simple text formats. * Users can modify gene sets or create new ones. * The results can be visualized within the software. * It is simple to compare multiple analyses of the same data set with different settings. * User-definable hyperlinks are provided to external sites to allow more efficient browsing of the results. * For programmers, there is a command line interface as well as a simple application programming interface that can be used to plug ermineJ functionality into your own code Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ErmineJ (RRID:SCR_006450) Copy   

•   Source: SciCrunch Registry