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http://biit.cs.ut.ee/graphweb/

GraphWeb allows the detection of modules from biological, heterogeneous and multi-species networks, and the interpretation of detected modules using Gene Ontology, cis-regulatory motifs and biological pathways. GraphWeb is a public web server for graph-based analysis of biological networks that: * analyses directed and undirected, weighted and unweighted heterogeneous networks of genes, proteins and microarray probesets for many eukaryotic genomes; * integrates multiple diverse datasets into global networks; * incorporates multispecies data using gene orthology mapping; * filters nodes and edges based on dataset support, edge weight and node annotation; * detects gene modules from networks using a collection of algorithms; * interprets discovered modules using Gene Ontology, pathways, and cis-regulatory motifs. Platform: Online tool

Proper citation: GraphWeb (RRID:SCR_005746) Copy   

•   Source: SciCrunch Registry

http://www.lasige.di.fc.ul.pt/webtools/proteinon/

ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions. The query compute protein semantic similarity returns the semantic similarity scores between all proteins entered, in matrix format. The option Measure allows users to choose one of several semantic similarity measures: Resnik, Lin, or Jiang & Conrath's measures with or without the DCA approach, plus the graph-based simUI and simGIC measures. These measures are listed by order of performance as evaluated with protein sequence similarity. The option GO type allows users to choose one of the aspects of GO: molecular function, biological process and cellular component. The option Ignore IEA limits the query to non-electronic annotations, excluding evidence types: IEA, NAS, ND, NR.

Proper citation: ProteInOn (RRID:SCR_005740) 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.animalgenome.org/bioinfo/tools/catego/

CateGOrizer takes batch input of GO term IDs in a list format or unformatted plain text file, allows users to choose one of the available classifications such as GO_slim, GOA, EGAD, MGI_GO_slim, GO-ROOT, or a self-defined classification list, find its parental branch and performs an accumulative classification count, and returns the results in a sorted table of counts, percentages, and a pie chart (if it takes longer than standard time out period, it will email the user with a URL link to the results). This tool is comprised with a set of perl CGI programs coupled with a MySQL DBMS that stores the GO terms DAG data. Platform: Online tool

Proper citation: CateGOrizer (RRID:SCR_005737) Copy   

•   Source: SciCrunch Registry

http://www.berkeleybop.org/goose/

A web utility providing a direct interface to perform SQL queries directly on the GO database, allowing users to run custom queries without having to install a copy of the GO database locally. GOOSE includes many sample queries to aid novice users and allows results to be retrieved as a web page or as tab-delimited text. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GO Online SQL Environment (GOOSE) (RRID:SCR_006174) 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

http://probeexplorer.cicancer.org/principal.php

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query. Platform: Online tool

Proper citation: ProbeExplorer (RRID:SCR_007116) Copy   

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http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://funspec.med.utoronto.ca/

FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms. FunSpec (an acronym for Functional Specification) inputs a list of yeast gene names, and outputs a summary of functional classes, cellular localizations, protein complexes, etc. that are enriched in the list. The classes and categories evaluated were downloaded from the MIPS Database and the GO Database . In addition, many published datasets have been compiled to evaluate enrichment against. Hypertext links to the publications are given. The p-values, calculated using the hypergeometric distribution, represent the probability that the intersection of given list with any given functional category occurs by chance. The Bonferroni-correction divides the p-value threshold, that would be deemed significant for an individual test, by the number of tests conducted and thus accounts for spurious significance due to multiple testing over the categories of a database. After the Bonferroni correction, only those categories are displayed for which the chance probability of enrichment is lower than: p-value/#CD where #CD is the number of categories in the selected database. Without the Bonferroni Correction, all categories are displayed for which the same probability of enrichment is lower than: p-value threshold in an individual test Note that many genes are contained in many categories, especially in the MIPS database (which are hierarchical) and that this can create biases for which FunSpec currently makes no compensation. Also the databases are treated as independent from one another, which is really not the case, and each is searched seperately, which may not be optimal for statistical calculations. Nonetheless, we find it useful for sifting through the results of clustering analysis, TAP pulldowns, etc. Platform: Online tool

Proper citation: FunSpec (RRID:SCR_006952) Copy   

•   Source: SciCrunch Registry

https://omictools.com/ecgene-tool

Database of functional annotation for alternatively spliced genes. It uses a gene-modeling algorithm that combines the genome-based expressed sequence tag (EST) clustering and graph-theoretic transcript assembly procedures. It contains genome, mRNA, and EST sequence data, as well as a genome browser application. Organisms included in the database are human, dog, chicken, fruit fly, mouse, rhesus, rat, worm, and zebrafish. Annotation is provided for the whole transcriptome, not just the alternatively spliced genes. Several viewers and applications are provided that are useful for the analysis of the transcript structure and gene expression. The summary viewer shows the gene summary and the essence of other annotation programs. The genome browser and the transcript viewer are available for comparing the gene structure of splice variants. Changes in the functional domains by alternative splicing can be seen at a glance in the transcript viewer. Two unique ways of analyzing gene expression is also provided. The SAGE tags deduced from the assembled transcripts are used to delineate quantitative expression patterns from SAGE libraries available publicly. The cDNA libraries of EST sequences in each cluster are used to infer qualitative expression patterns.

Proper citation: ECgene: Gene Modeling with Alternative Splicing (RRID:SCR_007634) 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

http://www.medinfopoli.polimi.it/GFINDer/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

Proper citation: GFINDer: Genome Function INtegrated Discoverer (RRID:SCR_008868) Copy   

•   Source: SciCrunch Registry

http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

•   Source: SciCrunch Registry

http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible

Proper citation: STRAP (RRID:SCR_005675) Copy   

•   Source: SciCrunch Registry

http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 30, 2015. Refer to Proteome Analyst 3.0. Subcellular Localization and GO General Molecular Function predictions for many model organism proteomes using Protein Analyst, with a very high coverage rate. When users blast their proteins against the database of results, they will not only be shown blast homologs from the model organisms, but also the Subcellular Localization and GO General Molecular Function predictions as well.

Proper citation: Proteome Analyst PA-GOSUB (RRID:SCR_008234) Copy   

•   Source: SciCrunch Registry

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://vortex.cs.wayne.edu/projects.htm#Onto-Design

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool

Proper citation: Onto-Design (RRID:SCR_000601) 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