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http://www.brc.riken.jp/inf/en
RIKEN BRC contributes to advancement of life science research by collecting, preserving and distributing biological resources such as experimental animals, experimental plants, cultured cell lines, genetic materials (DNA), and associated bioinformatics. The RIKEN BRC develops novel bioresources to promote scientific research and new technologies to increase the value of bioresources, and also to implement effective procedures for the preservation, quality control and usage of bioresources. The RIKEN BRC is working closely with institutions in Japan and abroad.
Proper citation: RIKEN BioResource Center (RRID:SCR_003250) Copy
https://www.proteinspire.org/MOPED/
An expanding multi-omics resource that enables rapid browsing of gene and protein expression information from publicly available studies on humans and model organisms. MOPED also serves the greater research community by enabling users to visualize their own expression data, compare it with existing studies, and share it with others via private accounts. MOPED uniquely provides gene and protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis utilizing SPIRE (Systematic Protein Investigative Research Environment). Data can be queried for specific genes and proteins; browsed based on organism, tissue, localization and condition; and sorted by false discovery rate and expression. MOPED links to various gene, protein, and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED (MOPED 2.5) The current version of MOPED (MOPED 2.5, 2014) contains approximately 5 million total records including ~260 experiments and ~390 conditions.
Proper citation: MOPED - Model Organism Protein Expression Database (RRID:SCR_006065) Copy
Web tool to search multiple public variant databases simultaneously and provide a unified interface to facilitate the search process. Used for integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Used for analysis of human genes and variants by cross-disciplinary integration of records available in public databases to facilitate clinical diagnosis and basic research.
Proper citation: MARRVEL (RRID:SCR_016871) Copy
Web application for Quantitative Trait Loci mapping via bulk segregant analysis of yeast sequencing data. Application provides automated data processing, annotations, and web interface to explore identified QTLs.
Proper citation: BSA4Yeast (RRID:SCR_017113) Copy
Web based gene set analysis toolkit designed for functional genomic, proteomic, and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated. WebGestalt incorporates information from different public resources and provides a way for biologists to make sense out of gene lists. This version of WebGestalt supports eight organisms, including human, mouse, rat, worm, fly, yeast, dog, and zebrafish.
Proper citation: WebGestalt: WEB-based GEne SeT AnaLysis Toolkit (RRID:SCR_006786) Copy
Microarray data management and analysis system for NCI / Center for Cancer Research scientists / collaborators. Data is secured and backed up on a regular basis, and investigators can authorize levels of access privileges to their projects, allowing data privacy while still enabling data sharing with collaborators.
Proper citation: mAdb (RRID:SCR_006677) Copy
http://purl.bioontology.org/ontology/PHENOMEBLAST
A cross-species phenotype and anatomy ontology resulting from combining available anatomy and phenotype ontologies and their definitions. The ontology includes phenotype definitions for yeast, mouse, fish, worm, fly and human phenotypes and diseases.
Proper citation: PhenomeBLAST Ontology (RRID:SCR_005139) Copy
PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing
Proper citation: phenomeNET (RRID:SCR_006165) Copy
Database of genetic and molecular biological information about Candida albicans. Contains information about genes and proteins, descriptions and classifications of their biological roles, molecular functions, and subcellular localizations, gene, protein, and chromosome sequence information, tools for analysis and comparison of sequences and links to literature information. Each CGD gene or open reading frame has an individual Locus Page. Genetic loci that are not tied to DNA sequence also have Locus Pages. Provides Gene Ontology, GO, to all its users. Three ontologies that comprise GO (Molecular Function, Cellular Component, and Biological Process) are used by multiple databases to annotate gene products, so that this common vocabulary can be used to compare gene products across species. Development of ontologies is ongoing in order to incorporate new information. Data submissions are welcome.
Proper citation: Candida Genome Database (RRID:SCR_002036) Copy
https://www.genevestigator.com/gv/
A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.
Proper citation: Genevestigator (RRID:SCR_002358) Copy
http://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home
Database of proteins found in the nucleoli of Arabidopsis, identified through proteomic analysis. The Arabidopsis Nucleolar Protein database (AtNoPDB) provides information on the plant proteins in comparison to human and yeast proteins, and images of cellular localizations for over a third of the proteins. A proteomic analysis was carried out of nucleoli purified from Arabidopsis cell cultures and to date 217 proteins have been identified. Many proteins were known nucleolar proteins or proteins involved in ribosome biogenesis. Some proteins, such as spliceosomal and snRNP proteins, and translation factors, were unexpected. In addition, proteins of unknown function which were either plant-specific or conserved between human and plant, and proteins with differential localizations were identified.
Proper citation: Arabidopsis Nucleolar Protein Database (RRID:SCR_001793) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Database to access gene information through common names and allows identification of homologs and paralogs for a given gene. This publicly available tool leverages public sequence data, gene metadata information, and other publicly available data to calculate and display orthologous and paralogous gene relationships for all genes from several species, including yeasts, insects, worms, vertebrates, mammals, and primates such as humans.
Proper citation: GeneSeer (RRID:SCR_002626) Copy
http://www.ihop-net.org/UniPub/iHOP/
Information system that provides a network of concurring genes and proteins extends through the scientific literature touching on phenotypes, pathologies and gene function. It provides this network as a natural way of accessing millions of PubMed abstracts. By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource, bringing all advantages of the internet to scientific literature research. Moreover, this literature network can be superimposed on experimental interaction data (e.g., yeast-two hybrid data from Drosophila melanogaster and Caenorhabditis elegans) to make possible a simultaneous analysis of new and existing knowledge. The network contains half a million sentences and 30,000 different genes from humans, mice, D. melanogaster, C. elegans, zebrafish, Arabidopsis thaliana, yeast and Escherichia coli.
Proper citation: Information Hyperlinked Over Proteins (RRID:SCR_004829) Copy
A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.
Proper citation: Unified Human Interactome (RRID:SCR_005805) Copy
http://genomics.senescence.info/genes/
Collection of annotated and manually curated data of genes related to aging divided into genes related to longevity and/or aging in model organisms (yeast, worms, flies, mice, etc.) and aging related human genes.
Proper citation: GenAge (RRID:SCR_010223) Copy
A gene and protein interactions database designed specifically for the model organism Drosophila including protein-protein, transcription factor-gene, microRNA-gene, and genetic interactions. For advanced searches and dynamic graphing capabilities the IM Browser and a DroID Cytoscape plugin are available.
Proper citation: DroID - Drosophila Interactions Database (RRID:SCR_006634) Copy
http://bowtie-bio.sourceforge.net/recount/
RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.
Proper citation: ReCount - A multi-experiment resource of analysis-ready RNA-seq gene count datasets (RRID:SCR_001774) Copy
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