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http://jbirc.jbic.or.jp/hinv/evola/

Evola is a sub-database of H-InvDB, providing ortholog data as evolutionary annotation. Representative transcripts (one transcript per one gene locus) were analyzed as genes. Orthologs were first detected by computational analysis. Then, more reliable orthologs were determined by manual curation inspecting the phylogenetic trees., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Evola (RRID:SCR_007651) Copy   

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http://www.everest.cs.huji.ac.il

EVEREST is an automatic process of identifying and classifying of protein domains. Users can search for specific proteins using Protein ID or name, browse through protein families, and upload/download protein sequence data. EVEREST combines methodologies from the fields of finite metric spaces, machine learning and statistical modeling and achieves state of the art results. The process begins by constructing a database of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families, choosing the best putative families using machine learning techniques, and creating a statistical model for each of the chosen families. This procedure is then iterated: The aforementioned statistical models are used to scan all protein sequences, to recreate a segment database and to cluster them again. Performance was evaluated by comparing with Pfam and SCOP.

Proper citation: EVEREST - EVolutionary Ensembles of REcurrent SegmenTs (RRID:SCR_007650) Copy   

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http://euhcvdb.ibcp.fr

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented August 23, 2016. The euHCVdb is oriented towards protein sequence, structure, function analysis and structural biology of the Hepatitis C Virus. It is monthly updated from the EMBL Nucleotide sequence database and maintained in a relational database management system (PostgreSQL). Programs for parsing the EMBL database flat files, annotating HCV entries, filling up and querying the database used SQL and Java programming languages. Great efforts have been made to develop a fully automatic annotation procedure thanks to a reference set of HCV complete annotated well-characterized genomes of various genotypes. This automatic procedure ensures standardization of nomenclature for all entries and provides genomic regions/proteins present in the entry, bibliographic reference, genotype, interesting sites (e.g. HVR1) or domains (e.g. NS3 helicase), source of the sequence (e.g. isolate) and structural data that are available as protein 3D models. The euHCVdb is funded as part of the HepCVax cluster (EC grant QLK2-CT-2002-01329) and viRgil network of excellence (EC grant LSHM-CT-2004-503359).

Proper citation: euHCVdb: The European HCV database (RRID:SCR_007645) Copy   

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http://www.cbil.upenn.edu/EpoDB/

Database of genes that relate to vertebrate red blood cells. It includes DNA sequence, structural features, protein information, gene expression information and transcription factor binding sites. This database is no longer maintained or updated.

Proper citation: EpoDB - Erythropoiesis Database (RRID:SCR_007642) Copy   

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http://bioinfo.mc.vanderbilt.edu/ERGR/

The aim of the Ethanol-Related Gene Resource (ERGR) database is to provide a comprehensive and useful gene resource to the Ethanol/Alcohol research community. Currently, the ERGR database contains more than 30 large datasets from literature and 21 mouse QTLs from public database. These data are from 5 organisms (human, mouse, rat, fly and worm) and produced by multiple approaches (expression, association, linkage, QTL, literature search etc). Users can browse or search the database in different levels. Moreover, ERGR provides data integration (union and intersection) and candidate gene selection based on multiple datasets or organisms.

Proper citation: ERGR- Ethanol-Related Genome Resource (RRID:SCR_007643) Copy   

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http://www-lecb.ncifcrf.gov/mitoDat/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It is dedicated to the nuclear genes specifying the enzymes, structural proteins, and other proteins, many still not identified, involved in mitochondrial biogenesis and function. MitoDat highlights predominantly human nuclear-encoded mitochondrial proteins, although it also includes proteins from other animals in addition to those currently known only from yeast and other fungal mitochondria, as well as from plant mitochondria. he database consolidates information from various biological databases, eg., GenBank, SwissPro, Genome Data Base (GDB), Online Mendelian Inheritance in Man (OMIM), et al. Because the mitochondrion has a central role in cellular metabolism, it is involved in many human diseases. This database should help us in studying these diseases. We are also hyperlinked to the Report of the committee on human mitochondrial DNA, maintained by the Wallace group at Emory. It can be accessed here and also from the results when searching mitoDat for mitochondrially encoded genes. The Report of the committee on human mitochondrial DNA is currently the most comprehensive source of information on mitochondrial DNA mutations, other defects, and disorders in which the mitochondrial DNA deficiencies have been associated.

Proper citation: MitoDat - Mendelian Inheritance and the Mitochondrion (RRID:SCR_007799) Copy   

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http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=mirgenv3

An integrated database of positional relationships between animal miRNAs and genomic annotation sets and animal miRNA targets according to combinations of widely used target prediction programs. miRGen has three connected interfaces which query this data. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands, and pseudogenes. The Targets interface provides access to unions and intersections of four widely used target prediction programs, and experimentally supported targets from TarBase. The Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance, and provides specific functional information on the targets of miRNAs within each cluster.

Proper citation: miRGen (RRID:SCR_007796) Copy   

•   Source: SciCrunch Registry

http://www.ba.itb.cnr.it/mitochondriome/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A web site dedicated to providing links to mitochondrial data and databases, as well as links to other mitochondrial sites and relevant information. It provides links to databases, complete mitochondrial genomes, genome maps, and publications.

Proper citation: Mitochondriome (RRID:SCR_007798) Copy   

•   Source: SciCrunch Registry

http://www.mir2disease.org/

A manually curated database, aims at providing a comprehensive resource of miRNA deregulation in various human diseases. Each entry in the miR2Disease contains detailed information on a miRNA-disease relationship, including miRNA ID, disease name, a brief description of the miRNA-disease relationship, miRNA expression pattern in the disease state, detection method for miRNA expression, experimentally verified miRNA target gene(s), and literature reference . All entries can be retrieved by miRNA ID, disease name or target gene. miR2Disease will be updated bimonthly. miR2Disease sincerely looks forward to recently established relationship between miRNA and human diseases to be submitted.

Proper citation: miR2Disease (RRID:SCR_007792) Copy   

•   Source: SciCrunch Registry

http://mirgator.kobic.re.kr/

Database of compiled, public, deep sequencing miRNA data and several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data are of great utility for studying iso-miRs, miRNA editing and modifications. miRNA����??target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets.

Proper citation: miRGator (RRID:SCR_007793) Copy   

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http://bioinfo.ibp.ac.cn/NPInter/demo/

A database covering eight category functional interactions between noncoding RNAs (except tRNAs and rRNAs) and proteins related biomacromolecules (proteins, mRNAs and genomic DNAs) in six model organisms. Functional interactions imply both physical interactions between the ncRNA and protein, and other forms of interaction where the combination of an ncRNA and an mRNA or a genomic DNA sequence elicits a cellular reaction. This database is distinguished from other biomolecular interaction database by: 1. The data of NPInter is novel, in the sense that no earlier database has especially cataloged this type of data (ncRNA-protein interactions). The database now contains more than 700 published functional interactions from the six organisms E. coli, yeast, worm, fly, mouse and human in which functional interactions experiments have been concentrated. The amount of data is not large, but the NPInter covers almost all experimentally verified ncRNA functional interaction data which had been published before the end of last year. 2. The ncRNA functional interaction data are entered into NPInter only following publication in books or peer-reviewed journals. Entry is done manually by a curator, and thereafter double-checked by a second curator. 3. We introduce a classification of the functional interaction data, which is based on the functional interaction process the ncRNA takes part in. 4. NPInter also provides an efficient search option, allowing recovery of interactions, related publications and other information., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NPInter (RRID:SCR_007825) Copy   

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http://oomycetes.genomeprojectsolutions-databases.com/

The Oomycete Genomics Database is a publicly accessible resource that includes functional assays and expression data, combined with transcript and genomic analysis and annotation. OGD builds upon data available from the Phytophthora Genome Consortium, Syngenta Phytophthora Consortium and the Phytophthora Functional Genomics Database. Data are analyzed and annotated using NCGR''s XGI System. The knowledge gained from these studies provide significant insight into key molecular processes regulating an economically important pathosystem and will provide novel tools for improvement of disease resistance in crop plants.

Proper citation: OGD - Oomycete Genomics Database (RRID:SCR_007828) Copy   

•   Source: SciCrunch Registry

http://www.noncode.org/

Collection of non-coding RNAs (excluding tRNAs and rRNAs) as an integrated knowledge database. Used to get text information such as class,name,location,related publication,mechanism through which it exerts its function, view figures which show their location in the genome or in a specific DNA fragment, and the regulation elements flanking the ncRNA gene sequences.

Proper citation: NONCODE (RRID:SCR_007822) Copy   

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http://www.imtech.res.in/raghava/mhcbn/

The MHCBN is a curated database consisting of detailed information about Major Histocompatibility Complex (MHC) Binding,Non-binding peptides and T-cell epitopes. The version 4.0 of database provides information about peptides interacting with TAP and MHC linked autoimmune diseases.

Proper citation: MHCBN: A comprehensive database of MHC binding and non-binding peptides (RRID:SCR_007785) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.leeds.ac.uk/metatiger

metaTIGER is a collection of metabolic profiles and phylogenomic information on a taxonomically diverse range of eukaryotes. Phylogenomic information is provided by 2,257 large phylogenetic trees which can be interactively explored. High-throughput tree analysis can also be carried out to identify trees of interest, e.g. trees containing horizontal gene transfers. metaTIGER also provides novel facilities for viewing and comparing the metabolic profiles.

Proper citation: metaTIGER (RRID:SCR_007781) Copy   

•   Source: SciCrunch Registry

http://metallo.scripps.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 24, 2013. Database and Browser containing quantitative information on all the metal-containing sites available from structures in the PDB distribution. This database contains geometrical and molecular information that allows the classification and search of particular combinations of site characteristics, and answer questions such as: How many mononuclear zinc-containing sites are five coordinate with X-ray resolution better than 1.8 Angstroms?, and then be able to visualize and manipulate the matching sites. The database also includes enough information to answer questions involving type and number of ligands (e.g. "at least 2 His"), and include distance cutoff criteria (e.g. a metal-ligand distance no more than 3.0 Angstroms and no less than 2.2 Angstroms). This database is being developed as part of a project whose ultimate goal is metalloprotein design, allowing the interactive visualization of geometrical and functional information garnered from the MDB. The database is created by automatic recognition and extraction of metal-binding sites from metal-containing proteins. Quantitative information is extracted and organized into a searchable form, by iterating through all the entries in the latest PDB release (at the moment: September 2001). This is a comprehensive quantitative database, which exists in SQL format and contains information on about 5,500 proteins.

Proper citation: Metalloprotein Site Database (RRID:SCR_007780) Copy   

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http://mips.gsf.de/genre/proj/mfungd

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16, 2019.Database for annotated mouse proteins and their occurrence in protein networks. It contains cDNA and protein sequences, annotation, gene models and mapping, FunCat, UCSC Genome Viewer, SIMAP, pseudogenes (Genome Viewer Track), InterPro, and splice variants. Protein function annotation is performed using the Functional Catalogue (FunCat) annotation scheme, which is a hierarchically structured classification system. To provide up-to-date similarity search results and InterPro domain analyses, the protein entries are interconnected with the SIMAP database. The gene models are based on the RefSeq mouse cDNAs. The work of our group is focussed on the annotation of biological systems. Therefore, results from the Mammalian Protein-Protein Interaction Database and the Comprehensive Resource of Mammalian Protein Complexes are linked to the MfunGD dataset. Links to external resources are also provided. MfunGD is implemented in GenRE, a J2EE based component oriented multi-tier architecture.

Proper citation: MfunGD - MIPS Mouse Functional Genome Database (RRID:SCR_007783) Copy   

•   Source: SciCrunch Registry

http://www.receptors.org/NR/

A database of information on nuclear receptors. Included in the database are sequence information, structural information, and mutation data. Users can BLAST sequences, view 2D structural data, see the chromosomal location of nuclear receptors genes, and utilize other tools found on the website.

Proper citation: NucleaRDB (RRID:SCR_007782) Copy   

•   Source: SciCrunch Registry

http://networkin.info/

A method for predicting in vivo kinase-substrate relationships, that augments consensus motifs with context for kinases and phosphoproteins. This website allows a user to browse/search and investigate predictions made using the NetworKIN algorithm. The site is powered by the latest phosphoproteome in Phospho.ELM. Alternatively users can submit their own protein sequences and phosphorylation sites and obtain new NetworKIN predictions.

Proper citation: NetworKIN (RRID:SCR_007818) Copy   

•   Source: SciCrunch Registry

https://www.oxfordjournals.org/our_journals/nar/database/summary/954

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. A database for the study of protein inter-atomic distance distribution. Currently, the distances are extracted from the protein structures determined through X-ray Crystallography, but they could also be obtained from NMR structural models. The known structures with the resolution higher than 2A and less than 70% sequence similarities are selected. Each type of distances is specified in terms of the types of the atoms it involves, the types of the residues containing the atoms, and the types of the residues in between the two end residues in sequence. An automated system is built to generate and process the data dynamically. The system consists of two levels of databases. The first one stores the sequence and structure information for a large set of high-resolution protein structures, with a similar data structure as the structural data represented in the PDB Data Bank. The second one stores the information for the distance distributions, with each record corresponding to a distribution function. The second database is built dynamically from the first one. The database can provide structural information in terms of distance distributions to structural biologists. Such information can be valuable for the study of many fundamental biological problems including protein structure prediction and determination, protein dynamics simulation, molecular design, protein structural analysis and classification, etc.

Proper citation: PIDD (RRID:SCR_007854) Copy   

•   Source: SciCrunch Registry