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http://www.sebida.de/

A database for the functional and evolutionary analysis of sex-biased genes. Sebida integrates data from multiple microarray studies comparing male versus female gene expression in D. melanogaster, D. simulans, and A. gambiae. In addition to the ratio of male to female (or testes to ovaries) expression for each gene, Sebida provides information useful for evolutionary studies, including measures of recombination, codon bias, and interspecific divergence.

Proper citation: SEBIDA: Sex Bias Database (RRID:SCR_007918) Copy   

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http://www.selenodb.org/

A database of eukaryotic selenoprotein genes, proteins, SECIS elements and related molecules. Selenoproteins are routinely mispredicted by automatic annotation systems and, therefore, misannotated in most genomic databases. We aim to provide correct annotations for the growing number of known selenoprotein genes. Current efforts are directed towards the construction of an initial set of genomic annotations in selected sequenced organisms using ad hoc computational tools and manually curated predictions. Computational approaches include ab initio and comparative gene prediction together with RNA secondary structure predictions.

Proper citation: SelenoDB (RRID:SCR_007919) Copy   

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http://ymbc.ym.edu.tw/sca_ensembl/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A candidate gene database for Spinocerebellar ataxia (SCA), which collected 3185 genes for 17 types of SCA. Those SCA subtypes that have known disease genes can be used as positive controls to optimize the parameters. The users may browse the candidate genes of a given SCA subtype by using the default parameters. The known disease genes were found to be the top three candidates using the default parameters. Alternatively, the users may score the candidate genes by changing the weight or the scores on the basis of their own working hypothesis.

Proper citation: SCAdb: A CANDIDATE GENE DATABASE FOR SPINOCEREBELLAR ATAXIAS (RRID:SCR_007915) Copy   

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http://www.chibi.ubc.ca/Gemma

Resource for reuse, sharing and meta-analysis of expression profiling data. Database and set of tools for meta analysis, reuse and sharing of genomics data. Targeted at analysis of gene expression profiles. Users can search, access and visualize coexpression and differential expression results.

Proper citation: Gemma (RRID:SCR_008007) Copy   

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http://www.grt.kyushu-u.ac.jp/spad/

It is divided to four categories based on extracellular signal molecules (Growth factor, Cytokine, and Hormone) and stress, that initiate the intracellular signaling pathway. SPAD is compiled in order to describe information on interaction between protein and protein, protein and DNA as well as information on sequences of DNA and proteins. There are multiple signal transduction pathways: cascade of information from plasma membrane to nucleus in response to an extracellular stimulus in living organisms. Extracellular signal molecule binds specific intracellular receptor, and initiates the signaling pathway. Now, there is a large amount of information about the signaling pathway which controls the gene expression and cellular proliferation. We have developed an integrated database SPAD to understand the overview of signaling transduction.

Proper citation: Signaling Pathway Database (RRID:SCR_008243) Copy   

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http://interactome-cmp.ucsf.edu/

This database currently holds E-MAP scores (individual interactions and correlation coefficients) for budding yeast genes involved in the early secretory pathway and chromosome function (including DNA damage and repair, transcriptional control, chromosome segregation and telomere regulation). E-MAPs (Epistatic Mini Array Profiles) are formed by creating and quantifying high-density genetic interaction maps. With this method, observed double mutant colony sizes are compared to those that would be expected from a distribution of typical double mutant colonies of each strain. Each interaction is assigned a score, which indicates the magnitude of the difference from the expected value and the certainty of the score. Negative (or aggravating) scores (< -2.5) correspond to synthetic sick/lethal interactions while positive (or alleviating) scores (> +2.5) corresponds to epistatic or suppressor interactions.

Proper citation: Krogan Lab Interactome Database (RRID:SCR_008121) Copy   

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http://mrna.otago.ac.nz/

Database that provides access to mRNA sequences and associated regulatory elements that were processed from Genbank. These mRNA sequences include complete genomes, which are divided into 5-prime UTRs, 3-prime UTRs, initiation sequences, termination regions and full CDS sequences. This data can be searched for a range of properties including specific mRNA sequences, mRNA motifs, codon usage, RSCU values, information content, etc.

Proper citation: Transterm (RRID:SCR_008244) Copy   

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http://escience.invitrogen.com/ipath/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. LINNEA Pathways is a user-friendly comprehensive online resource for gene- or protein-based scientific research. It is based on a total of 248 signaling and metabolic human biological pathway maps created for Invitrogen by GeneGo. The current version of iPath features 225 maps displaying human regulatory and metabolic pathways established in experimental literature produced by MetaCore from GeneGo, Inc. The map objects (proteins, genes, EC functions, and compounds) are connected via metabolic transformations and physical protein interactions, which were assembled by the GeneGo team of experienced annotators, geneticists, and biochemists. The pathways are organized in a vertical fashion following the general signaling path from signaling molecules and membrane receptors, via signal transduction cascades, to transcription factors and their gene targets. Following the natural organization of cellular machinery with highly interconnected pathways and modules, many maps are linked together via hyperlinked box symbols. Such linkage allows the reconstruction of a big picture view of human cell biology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Invitrogen iPath (RRID:SCR_008120) Copy   

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http://itfp.biosino.org/itfp/

ITFP is an integrated transcription factor (TF) platform, which included abundant TFs and targets message of mammalian. Support vector machine (SVM) algorithm combined with error-correcting output coding (ECOC) algorithm was utilized to identify and classify transcription factor from protein sequence of Human, Mouse and Rat. For transcription factor targets, a reverse engineering method named ARACNE was used to derive potential interaction pairs between transcription factor and downstream regulated gene from Human, Mouse and Rat gene expression profile data. Detailed information of gene expression profile data can be found in help page. Moreover, all data provided by the platform is free for non-commercial users and can be downloaded through links on help page.

Proper citation: Intergrated Transcription Factor Platform (RRID:SCR_008119) Copy   

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http://wodaklab.org/iRefWeb/

iRefWeb is an interface to a relational database containing the latest build of the interaction Reference Index (iRefIndex) which integrates protein interaction data from nine different interaction databases: BioGRID, BIND, CORUM, DIP, HPRD, INTACT, MINT, MPPI, MPACT and OPHID. Integration is achieved through a rigorously documented procedure for mapping protein IDs across databases, enabling systematic backtracking of the links used to establish the identity of the interaction partners. The iRefWeb interface groups interaction records from the different databases into a single non-redundant view. In particular iRefWeb facilitates comparing interaction records as seen by the various source databases relative to the PubMeds they were annotated from. iRefWeb is one of several views of the iRefIndex resource. Data are also available in a tab-delimited plain-text format (PSI-MITAB) as well as planned releases of a PSI-XML formatted version and a Cytoscape plugin. Further details about the iRefIndex project as well as data downloads are available from here . The method used to build iRefIndex is described in a recent publication.

Proper citation: Interaction Reference Index Web Interface (RRID:SCR_008118) Copy   

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http://www.ingenuity.com/

A horizontally and vertically structured database that pulls scientific and medical information and describes it consistently using the Ingenuity Ontology. The Knowledge Base pulls information from journals, public molecular content databases, and textbooks. Data is curated and and integrated into the Knowledge Base .

Proper citation: Ingenuity Pathways Knowledge Base (RRID:SCR_008117) Copy   

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http://nesg.org:9090/HCPIN/

The Human Cancer Pathway Protein Interaction Network (HCPIN) was constructed as a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology. It was constructed by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research.

Proper citation: Human Cancer Protein Interaction Network (RRID:SCR_008116) Copy   

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http://www.primervfx.com/#welcome

PrimerParadise is an online PCR primer database for genomics studies. The database contains predesigned PCR primers for amplification of exons, genes and SNPs of almost all sequenced genomes. Primers can be used for genome-wide projects (resequencing, mutation analysis, SNP detection etc). The primers for eukaryotic genomes have been tested with e-PCR to make sure that no alternative products will be generated. Also, all eukaryotic primers have been filtered to exclude primers that bind excessively throughout the genome. Genes are amplified as amplicons. Amplicons are defined as only one genes exons containing maximaly 3000 bp long dna segments. If gene is longer than 3000 bp then it is split into the segments at length 3000 bp. So for example gene at length 5000 bp is split into two segment and for both segments there were designed a separate primerpair. If genes exons length is over 3000 bp then it is split into amplicons as well. Every SNP has one primerpair. In addition of considering repetitive sequences and mono-dinucleotide repeats, we avoid designing primers to genome regions which contain other SNPs. -There are two ways to search for primers: you can use features IDs ( for SNP primers Reference ID, for gene/exon primers different IDs (Ensembl gene IDs, HUGO IDs for human genes, LocusLink IDs, RefSeq IDs, MIM IDs, NCBI gene names, SWISSPROT IDs for bacterial genes, VEGA gene IDs for human and mouse, Sanger S.pombe systematic gene names and common gene names, S.cerevisiae GeneBanks Locus, AccNo, GI IDs and common gene names) -you can use genome regions (chromosome coordinates, chromosome bands if exists) -Currently we provide 3 primers collections: proPCR for prokaryotic organisms genes primers -euPCR for eukaryotic organisms genes/exons primers -snpPCR for eukaryotic organisms SNP primers Sponsors: PrimerStudio is funded by the University of Tartu.

Proper citation: PrimerStudio (RRID:SCR_008232) Copy   

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http://www.bioinformatics2.wsu.edu/cgi-bin/Athena/cgi/home.pl

Athena is a web-based application that warehouses disparate datatypes related to the control of gene expression. Athena provides several features to enable exploration of the regulatory mechanisms of Arabidopsis gene control. The first main tool we provide is visualization of promoter domains of selected genes. Database crossreference for these transcription factors is provided as well as a statistical test for enrichment of binding activity within the set of selected promoters. The data mining tools in Athena allow for selection of sets of genes based on two different factors. -Genes can be select by specifying a set of binding factors whose putative sites must be present within all of those genes'' promoter regions. -Alternatively, genes can be selected using Gene Ontology annotations. Both GO (Gene Ontology) Slim terms and Gene Ontology terms are available. One can select a set of genes by either choosing a union of the genes annotated by a selected set of Slim terms or Gene Ontology terms. The selected gene''s putative binding factors are listed, including enrichment data. Furthermore, enriched presence of Gene Ontology terms is given. The analysis suite provides both enhanced data mining tools for selecting genes as well as several data displays., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Athena (RRID:SCR_008110) Copy   

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http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?iter

ITER is a toxicology data file on the National Library of Medicine''s (NLM) Toxicology Data Network. It contains data in support of human health risk assessments. It is compiled by Toxicology Excellence for Risk Assessment (TERA) and contains over 600 chemical records with key data from the Agency for Toxic Substances & Disease Registry (ATSDR), Health Canada, National Institute of Public Health & the Environment (RIVM) - The Netherlands, U.S. Environmental Protection Agency (EPA), and independent parties whose risk values have undergone peer review. ITER provides a comparison of international risk assessment information in a side-by-side format and explains differences in risk values derived by different organizations. ITER data, focusing on hazard identification and dose-response assessment, is extracted from each agencys assessment and contains links to the source documentation. Among the key data provided in ITER are ATSDRs minimal risk levels; Health Canadas tolerable intakes/concentrations and tumorigenic doses/concentrations; EPAs carcinogen classifications, unit risks, slope factors, oral reference doses, and inhalation reference concentrations; RIVMs maximum permissible risk levels; NSF International''s reference doses and carcinogen risk levels, IARC''s cancer classifications, and noncancer and/or cancer risk values (that have undergone peer review) derived by independent parties. Users can search by chemical or other name, chemical name fragment, or Chemical Abstracts Service Registry Number(RN), and/or subject terms. Search results can easily be viewed, printed or downloaded. Search results are displayed in relevancy ranked order. Users may select to display exact term matches, complete records, or any combination of data from the following broad groupings: -Noncancer Oral -Cancer Oral -Noncancer Inhalation -Cancer Inhalation

Proper citation: International Toxicity Estimates for Risk (RRID:SCR_008196) Copy   

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http://www.jenner.ac.uk/

JenPep is a database of quantitative binding data for immunological protein-peptide interactions. It is a system which allows speedy access to this binding data through simple on-line interfaces and effective search mechanisms.

Proper citation: JenPep (RRID:SCR_008191) Copy   

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http://www-deletion.stanford.edu/YDPM/YDPM_index.html

This database contains different yeast strains searchable by ORF and gene name, and serves to support the Yeast Deletion and the Mitochondrial Proteomics Project. The database is hyperlinked with other public databases. The project aims to increase the understanding of mitochondrial function and biogenesis in the context of the cell. In the Deletion Project, strains from the deletion collection were monitored under 9 different media conditions selected for the study of mitochondrial function. 5791 heterozygous diploid and 4706 homozygous diploid deletion strains were monitored in parallel using molecular barcodes on fermentable (YPD, YPDGE) and non-fermentable substrates (YPG, YPE, YPL). The YDPM database contains both the raw data and growth rates calculated for each strain in each media condition. Strains can be searched by ORF or Gene name to access growth measurements and data plots for each strain. Category: Genomics Databases (non-vertebrate) Subcategory: Fungal genome databases Category: Organelle databases Subcategory: Mitochondrial genes and proteins

Proper citation: YDPM - Yeast Deletion Project (RRID:SCR_007977) Copy   

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http://alk.ibms.sinica.edu.tw

A database containing predicted viral miRNA candidate hairpins. Users may query the putative miRNA hairpins of a specific viral species by the hierarchical menu or by search function using the GenBank Identifier or RefSeq accession number. In addition, users can also search for the putative target genes of a particular viral miRNA hairpins by a RNAhybrid service link. We have previously identified human intronic microRNA as well as zebrafish microRNA. The microRNA hairpin discovery pipeline was also applied to discover viral encoded microRNAs. All viral genomes were obtain from NCBI. The classification of virus is based on the taxonomy table of NCBI (Jun, 2006). Totally, the genomes of 2266 viruses were analyzed. The 3’-UTR regions of human, mouse, rat, zebrafish, arabidopsis and rice genes are available for search.

Proper citation: Vir-Mir (RRID:SCR_007971) Copy   

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http://www.thearkdb.org/arkdb/

This website contains the mapping sequence of poultry. The ArkDB database system aims to provide a comprehensive public repository for genome mapping data from farmed and other animal species. In doing so, it aims to provide a route in to genomic and other sequence from the initial viewpoint of linkage mapping, RH mapping, physical mapping or - possibly more importantly - QTL mapping data. It's supported, in part, by the USDA-CSREES National Animal Genome Research Program in order to serve the poultry genome mapping community. This system represents a complete rewrite of the original version with the code migrated to java and the underlying database targeted at postgres (although any standards-compliant database engine should suffice). The initial release records details of maps and the markers that they contain. There are alternative entry points that target either a chromosome or a specific mapping analysis as the starting point. Limited relationships between markers are recorded and displayed. As with the previous version, all maps are drawn using data extracted from the database on the fly.

Proper citation: ChickBase (RRID:SCR_008147) Copy   

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http://microbialgenomics.energy.gov/index.shtml

Through its Microbial Genome Program (MGP) and its Genomics:GTL (GTL) program, DOEs Office of Biological and Environmental Research (BER) has sequenced more than 485 microbial genomes and 30 microbial communities having specialized biological capabilities. Identifying these genes will help investigators discern how gene activities in whole living systems are orchestrated to solve myriad life challenges. The MGP was begun in 1994 as a spinoff from the Human Genome Program. The goal of the program was to sequence the genomes of a number of nonpathogenic microbes that would be useful in solving DOE''s mission challenges in environmental-waste cleanup, energy production, carbon cycling, and biotechnology. Past projects include microbial genome program, microbial cell project, and the Laboratory Science Program at the DOE Joint Genome Institute. The two ongoing projects are Genomics: GTL program and Community Sequencing Program at the DOE Joint Genome Institute. Sponsors: Site sponsored by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Microbial Genomics Program (RRID:SCR_008140) Copy   

•   Source: SciCrunch Registry