Tools   Select Another Resource Report Type

http://www.ici.upmc.fr/cluego/

A Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ClueGO (RRID:SCR_005748) Copy   

•   Source: SciCrunch Registry

http://www.patika.org/

The human pathway database which contains different biological entities and reactions and software tools for analysis. PATIKA Database integrates data from several sources, including Entrez Gene, UniProt, PubChem, GO, IntAct, HPRD, and Reactome. Users can query and access this data using the PATIKAweb query interface. Users can also save their results in XML or export to common picture formats. The BioPAX and SBML exporters can be used as part of this Web service.

Proper citation: Pathway Analysis Tool for Integration and Knowledge Acquisition (RRID:SCR_002100) Copy   

•   Source: SciCrunch Registry

http://www.flymine.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

Proper citation: FlyMine (RRID:SCR_002694) Copy   

•   Source: SciCrunch Registry

http://www.innatedb.com

Publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralized resource. The database can be mined as a knowledgebase or used with the integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response. Although InnateDB curation focuses on innate immunity-relevant interactions and pathways, it also incorporates detailed annotation on the entire human, mouse and bovine interactomes by integrating data (178,000+ interactions & 3,900+ pathways) from several of the major public interaction and pathway databases. InnateDB also has integrated human, mouse and bovine orthology predictions generated using Ortholgue software. Ortholgue uses a phylogenetic distance-based method to identify possible paralogs in high-throughput orthology predictions. Integrated human and mouse conserved gene order and synteny information has also been determined to provide further support for orthology predictions. InnateDB Capabilities: * View statistics for manually-curated innate immunity relevant molecular interactions. New manually curated interactions are submitted weekly. * Search for genes and proteins of interest. * Search for experimentally-verified molecular interactions by gene/protein name, interaction type, cell type, etc. * Search genes/interactions belonging to 3,900 pathways. * Visualize interactions using an intuitive subcellular localization-based layout in Cerebral. * Upload your own list of genes along with associated gene expression data (from up to 10 experimental conditions) to interactively analyze this data in a molecular interaction network context. Once you have uploaded your data, you will be able to interactively visualize interaction networks with expression data overlaid; carry out Pathway, Gene Ontology and Transcription Factor Binding Site over-representation analyses; construct orthologous interaction networks in other species; and much more. * Access curated interaction data via a dedicated PSICQUIC webservice.

Proper citation: InnateDB (RRID:SCR_006714) Copy   

•   Source: SciCrunch Registry

http://www.bioguo.org/AnimalTFDB/

A comprehensive transcription factor (TF) database in which they identified and classified all the genome-wide TFs in 50 sequenced animal genomes (Ensembl release version 60). In addition to TFs, it also collects transcription co-factors and chromatin remodeling factors of those genomes, which play regulatory roles in transcription. Here they defined the TFs as proteins containing a sequence-specific DNA-binding domain (DBD) and regulating target gene expression. Currently, the AnimalTFDB classifies all the animal TFs into 72 families according to their conserved DBDs. Gene lists of transcription factors, transcription co-factors and chromatin remodeling factors of each species are available for downloading., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: AnimalTFDB (RRID:SCR_001624) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org

International database for laboratory mouse. Data offered by The Jackson Laboratory includes information on integrated genetic, genomic, and biological data. MGI creates and maintains integrated representation of mouse genetic, genomic, expression, and phenotype data and develops reference data set and consensus data views, synthesizes comparative genomic data between mouse and other mammals, maintains set of links and collaborations with other bioinformatics resources, develops and supports analysis and data submission tools, and provides technical support for database users. Projects contributing to this resource are: Mouse Genome Database (MGD) Project, Gene Expression Database (GXD) Project, Mouse Tumor Biology (MTB) Database Project, Gene Ontology (GO) Project at MGI, and MouseCyc Project at MGI.

Proper citation: Mouse Genome Informatics (MGI) (RRID:SCR_006460) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=renal

An integrated resource for information on genes, QTLs and strains associated with a variety of kidney and renal system conditions such as Renal Hypertension, Polycystic Kidney Disease and Renal Insufficiency, as well as Kidney Neoplasms.

Proper citation: Renal Disease Portal (RRID:SCR_009030) Copy   

•   Source: SciCrunch Registry

http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

•   Source: SciCrunch Registry

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

Web server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: KOBAS (RRID:SCR_006350) Copy   

•   Source: SciCrunch Registry

https://cansar.icr.ac.uk/

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Proper citation: canSAR (RRID:SCR_006794) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.intec.ugent.be/magic/

Web based interface for exploring and analyzing a comprehensive maize-specific cross-platform expression compendium. This compendium was constructed by collecting, homogenizing and formally annotating publicly available microarrays from Gene Expression Omnibus (GEO), and ArrayExpress.

Proper citation: Magic (RRID:SCR_006406) Copy   

•   Source: SciCrunch Registry

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

Database that provides access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez. A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. BioSystem records list and categorize components, such as the genes, proteins, and small molecules involved in a biological system. The companion FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems. A number of databases provide diagrams showing the components and products of biological pathways along with corresponding annotations and links to literature. This database was developed as a complementary project to (1) serve as a centralized repository of data; (2) connect the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system; and (3) facilitate computation on biosystems data. The NCBI BioSystems Database currently contains records from several source databases: KEGG, BioCyc (including its Tier 1 EcoCyc and MetaCyc databases, and its Tier 2 databases), Reactome, the National Cancer Institute's Pathway Interaction Database, WikiPathways, and Gene Ontology (GO). It includes several types of records such as pathways, structural complexes, and functional sets, and is desiged to accomodate other record types, such as diseases, as data become available. Through these collaborations, the BioSystems database facilitates access to, and provides the ability to compute on, a wide range of biosystems data. If you are interested in depositing data into the BioSystems database, please contact them.

Proper citation: NCBI BioSystems Database (RRID:SCR_004690) Copy   

•   Source: SciCrunch Registry

http://www.openphacts.org/

Project that developed an open access discovery platform, called Open Pharmacological Space (OPS), via a semantic web approach, integrating pharmacological data from a variety of information resources and tools and services to question this integrated data to support pharmacological research. The project is based upon the assimilation of data already stored as triples, in the form subject-predicate-object. The software and data are available for download and local installation, under an open source and open access model. Tools and services are provided to query and visualize this data, and a sustainability plan will be in place, continuing the operation of the Open PHACTS Discovery Platform after the project funding ends. Throughout the project, a series of recommendations will be developed in conjunction with the community, building on open standards, to ensure wide applicability of the approaches used for integration of data.

Proper citation: Open PHACTS (RRID:SCR_005050) Copy   

•   Source: SciCrunch Registry

http://pathways.mcdb.ucla.edu/algal/

Tools to search gene lists for functional term enrichment as well as to dynamically visualize proteins onto pathway maps. Additionally, integrated expression data may be used to discover similarly expressed genes based on a starting gene of interest.

Proper citation: Algal Functional Annotation Tool (RRID:SCR_012034) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?list=cover&q=*

Service that partners with the community to expose and simultaneously drill down into individual databases and data sets and return relevant content. This type of content, part of the so called hidden Web, is typically not indexed by existing web search engines. Every record links back to the originating site. In order for NIF to directly query these independently maintained databases and datasets, database providers must register their database or dataset with the NIF Data Federation and specify permissions. Databases are concept mapped for ease of sharing and to allow better understanding of the results. Learn more about registering your resource, http://neuinfo.org/nif_components/disco/interoperation.shtm Search results are displayed under the Data Federation tab and are categorized by data type and nervous system level. In this way, users can easily step through the content of multiple resources, all from the same interface. Each federated resource individually displays their query results with links back to the relevant datasets within the host resource. This allows users to take advantage of additional views on the data and tools that are available through the host database. The NIF site provides tutorials for each resource, indicated by the Professor Icon professor icon showing users how to navigate the results page once directed there through the NIF. Additionally, query results may be exported as an Excel document. Note: NIF is not responsible for the availability or content of these external sites, nor does NIF endorse, warrant or guarantee the products, services or information described or offered at these external sites. Integrated Databases: Theses virtual databases created by NIF and other partners combine related data indexed from multiple databases and combine them into one view for easier browsing. * Integrated Animal View * Integrated Brain Gene Expression View * Integrated Disease View * Integrated Nervous System Connectivity View * Integrated Podcasts View * Integrated Software View * Integrated Video View * Integrated Jobs * Integrated Blogs For a listing of the Federated Databases see, http://neuinfo.org/mynif/databaseList.php or refer to the Resources Listed by NIF Data Federation table below.

Proper citation: NIF Data Federation (RRID:SCR_004834) Copy   

•   Source: SciCrunch Registry

http://www.inmex.ca./INMEX/

A web-based tool to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner.

Proper citation: INMEX (RRID:SCR_004173) Copy   

•   Source: SciCrunch Registry

http://kt.ijs.si/software/SEGS/

A web tool for descriptive analysis of microarray data. The analysis is performed by looking for descriptions of gene sets that are statistically significantly over- or under-expressed between different scenarios within the context of a genome-scale experiments (DNA microarray). Descriptions are defined by using the terms from the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene-gene interactions found in the ENTREZ database. Gene annotations by GO and KEGG terms can also be found in the ENTREZ database. The tool provides three procedures for testing the enrichment of the gene sets (over- or under-expressed): Fisher's exact test, GSEA and PAGE, and option for combining the results of the tests. Because of the multiple-hypothesis testing nature of the problem, all the p-values are computed using the permutation testing method.

Proper citation: SEGS (RRID:SCR_003554) Copy   

•   Source: SciCrunch Registry

http://genetrail.bioinf.uni-sb.de/

A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneTrail (RRID:SCR_006250) Copy   

•   Source: SciCrunch Registry

http://gsgator.ewha.ac.kr/

A web-based platform for functional interpretation of gene sets with features such as cross-species Gene Set Analysis (GSA), Flexible and Interactive GSA, simultaneous GSA for multiple gene set, and and a fully integrated network viewer for both visualizing GSA results and molecular networks.

Proper citation: gsGator (RRID:SCR_012035) Copy   

•   Source: SciCrunch Registry

http://www.cdtdb.brain.riken.jp/CDT/Top.jsp

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

Proper citation: Cerebellar Development Transcriptome Database (RRID:SCR_013096) Copy   

•   Source: SciCrunch Registry