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http://bibiserv.techfak.uni-bielefeld.de/agt-sdp/

Database providing automatic test cases for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches. Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of the test cases, e.g. for testing docking algorithms. In unbound protein--protein docking, the complex of two proteins is predicted using the unbound conformations of the proteins (Halperin et al.,2002). For testing of docking algorithms, two unbound proteins which form a known complex have to be identified, so that the result of the docking algorithm can be compared to the known complex. For the identification of test cases, the structures taken from the PDB have to be classified as unbound proteins or complexes and unbound proteins with a 100% sequence identity to one complex part have to be searched. By now, most groups use handpicked test sets. The largest collection of test cases used so far is described by Chen et al. (Chen et al.,2003) and contains 31 test cases for unbound docking. Because of the exponential growth of available protein structures in the PDB, automatic generation of test cases will become more and more important in the future.

Proper citation: Automatic Generated Test-Sets Database for Protein-Protein Docking (RRID:SCR_002281) Copy   

•   Source: SciCrunch Registry

http://dirt.projectbamboo.org/

Registry of digital research tools for scholarly use that makes it easy for digital humanists and others conducting digital research to find and compare resources ranging from content management systems to music OCR, statistical analysis packages to mindmapping software., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bamboo DiRT (RRID:SCR_002556) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info/data.html

Complete set of free, publicly accessible, downloadable atlases, templates, and individual manually labeled brain image data, the largest collection of publicly available, manually labeled human brains in the world! http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/full

Proper citation: Mindboggle-101 atlases (RRID:SCR_002439) Copy   

•   Source: SciCrunch Registry

http://www.ecoli-york.org/

A database that curates new experimental and bioinformatic information about the genes and gene products of the model bacterium Escherichia coli K-12 strain MG1655. It has been created to integrate information from post-genomic experiments into a single resource with the aim of providing functional predictions for the 1500 or so gene products for which we have no knowledge of their physiological function. While EchoBASE provides a basic annotation of the genome, taken from other databases, its novelty is in the curation of post-genomic experiments and their linkage to genes of unknown function. Experiments published on E. coli are curated to one of two levels. Papers dealing with the determination of function of a single gene are briefly described, while larger dataset are actually included in the database and can be searched and manipulated. This includes data for proteomics studies, protein-protein interaction studies, microarray data, functional genomic approaches (looking at multiple deletion strains for novel phenotypes) and a wide range of predictions that come out of in silico bioinformatic approaches. The aim of the database is to provide hypothesis for the functions of uncharacterized gene products that may be used by the E. coli research community to further our knowledge of this model bacterium.

Proper citation: EchoBASE (RRID:SCR_002430) Copy   

•   Source: SciCrunch Registry

http://www.tanpaku.org/autophagy/

Database that provides basic, up-to-date information on relevant literature, and a list of autophagy-related proteins and their homologs in eukaryotes.

Proper citation: Autophagy Database (RRID:SCR_002671) Copy   

•   Source: SciCrunch Registry

http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry

http://greengenes.secondgenome.com/downloads

Database that provides access to the current and comprehensive 16S rRNA gene sequence alignment for browsing, blasting, probing, and downloading. The data and tools can assist the researcher in choosing phylogenetically specific probes, interpreting microarray results, and aligning/annotating novel sequences. The 16S rRNA gene database provides chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. ARB users can use Greengenes to update local databases., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Greengenes (RRID:SCR_002830) Copy   

•   Source: SciCrunch Registry

http://domine.utdallas.edu

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13,2026. Database of known and predicted protein domain (domain-domain) interactions containing interactions inferred from PDB entries, and those that are predicted by 8 different computational approaches using Pfam domain definitions. DOMINE contains a total of 26,219 domain-domain interactions (among 5,410 domains) out of which 6,634 are inferred from PDB entries, and 21,620 are predicted by at least one computational approach. Of the 21,620 computational predictions, 2,989 interactions are high-confidence predictions (HCPs), 2,537 interactions are medium-confidence predictions (MCPs), and the remaining 16,094 are low-confidence predictions (LCPs). (May 2014)

Proper citation: DOMINE: Database of Protein Interactions (RRID:SCR_002399) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/ieb/research/acembly/

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 29, 2016. AceView offers an integrated view of the human, nematode and Arabidopsis genes reconstructed by co-alignment of all publicly available mRNAs and ESTs on the genome sequence. Our goals are to offer a reliable up-to-date resource on the genes and their functions and to stimulate further validating experiments at the bench. AceView provides a curated, comprehensive and non-redundant sequence representation of all public mRNA sequences (mRNAs from GenBank or RefSeq, and single pass cDNA sequences from dbEST and Trace). These experimental cDNA sequences are first co-aligned on the genome then clustered into a minimal number of alternative transcript variants and grouped into genes. Using exhaustively and with high quality standards the available cDNA sequences evidences the beauty and complexity of mammals' transcriptome, and the relative simplicity of the nematode and plant transcriptomes. Genes are classified according to their inferred coding potential; many presumably non-coding genes are discovered. Genes are named by Entrez Gene names when available, else by AceView gene names, stable from release to release. Alternative features (promoters, introns and exons, polyadenylation signals) and coding potential, including motifs, domains, and homologies are annotated in depth; tissues where expression has been observed are listed in order of representation; diseases, phenotypes, pathways, functions, localization or interactions are annotated by mining selected sources, in particular PubMed, GAD and Entrez Gene, and also by performing manual annotation, especially in the worm. In this way, both the anatomy and physiology of the experimentally cDNA supported human, mouse and nematode genes are thoroughly annotated. Our goals are to offer an up-to-date resource on the genes, in the hope to stimulate further experiments at the bench, or to help medical research. AceView can be queried by meaningful words or groups of words as well as by most standard identifiers, such as gene names, Entrez Gene ID, UniGene ID, GenBank accessions.

Proper citation: AceView (RRID:SCR_002277) Copy   

•   Source: SciCrunch Registry

http://kb.phenoscape.org/

Knowledgebase that uses ontologies to integrate phenotypic data from genetic studies of zebrafish with evolutionary variable phenotypes from the systematic literature of ostariophysan fishes. Users can explore the data by searching for anatomical terms, taxa, or gene names. The expert system enables the broad scale analysis of phenotypic variation across taxa and the co-analysis of these evolutionarily variable features with the phenotypic mutants of model organisms. The Knowledgebase currently contains 565,158 phenotype statements about 2,527 taxa, sourced from 57 publications, as well as 38,189 phenotype statements about 4,727 genes, retrieved from ZFIN. 2013-01-26.

Proper citation: Phenoscape Knowledgebase (RRID:SCR_002821) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.charite.de/superdrug/

Public database of the structures of active ingredients in marketed drugs. It utilizes a web-query system with various search options, such as drug names, trade name, and synonyms.

Proper citation: Super Drug Database (RRID:SCR_002703) Copy   

•   Source: SciCrunch Registry

http://www.angis.org.au/Databases/Heart/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The aim of this locus-specific mutation database was to provide an online resource that contains summarized and updated information on familial hypertrophic cardiomyopathy (FHC)-associated mutations and related data, for researchers and clinicians. It also serves as a means of publishing previously unpublished data, which could be of value in understanding genotype/phenotype correlations. This database contains mutations in various genes known to cause familial hypertrophic cardiomyopathy, a genetic disorder associated with defects in the sarcomere [1]. Only gene symbols approved by HUGO are used and mutations are reported in accordance with guidelines recommended by the Mutation Database Initiative of HUGO and EBI.

Proper citation: Familial Hypertrophic Cardiomyopathy DNA Mutation Database (RRID:SCR_002346) Copy   

•   Source: SciCrunch Registry

http://dbtbs.hgc.jp/

Database of experimentally validated gene regulatory relations and the corresponding transcription factor binding sites upstream of Bacillus subtilis genes. The database allows the comparison of systematic experiments with individual experimental results in order to facilitate the elucidation of the complete B. subtilis gene regulatory network. The current version is constructed by surveying 947 references and contains the information of 120 binding factors and 1475 gene regulatory relations. For each promoter, all of its known cis-elements are listed according to their positions, while these cis-elements are aligned to illustrate the consensus sequence for each transcription factor. All probable transcription factors coded in the genome were classified using Pfam motifs. The DBTBS database was reorganized to show operons instead of individual genes as the building blocks of gene regulatory networks. It now contains 463 experimentally known operons, as well as their terminator sequences if identifiable. In addition, 517 transcriptional terminators were identified computationally. (De Hoon, M.J.L. et al., PLoS Comput. Biol. 1, e25 (2005)). A new section was added under "Motif conservation", which presents hexameric motifs found to be conserved to different extents between upstream intergenic regions of genus-specific subgroups of homologous proteins.

Proper citation: DBTBS (RRID:SCR_002345) Copy   

•   Source: SciCrunch Registry

http://www.wormatlas.org/

Anatomical atlas about structural anatomy of Caenorhabditis elegans. Provides simple interface allowing user to easily navigate through every anatomical structure of worm. Contains set of images which can be sorted by different characteristics: sex, genotype, age, body portion or tissue type. Includes links to other major worm websites and databases. Application for viewing and downloading thousands of unpublished electron micrographs and associated data. These images have been generated by several labs in the C. elegans community, including the MRC, the Hall lab (Center for C. elegans Anatomy), and the Culotti and Riddle labs.

Proper citation: WormAtlas (RRID:SCR_002861) Copy   

•   Source: SciCrunch Registry

http://viperdb.scripps.edu/

Database for icosahedral virus capsid structures. The emphasis of the resource is on providing data from structural and computational analyses on these systems, as well as high quality renderings for visual exploration. In addition, all virus capsids are placed in a single icosahedral orientation convention, facilitating comparison between different structures. The web site includes powerful search utilities , links to other relevant databases, background information on virus capsid structure, and useful database interface tools. It is an information source for the analysis of high resolution virus structures. VIPERdb is a one-stop site dedicated to helping users around the world examine the many icosahedral virus structures contained within the Protein Data Bank (PDB) by providing them with an easy to use database containing current data and a variety of analytical tools. Sponsors: VIPERdb is funded by the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIPERdb (RRID:SCR_002853) Copy   

•   Source: SciCrunch Registry

https://ndriresource.org/for-researchers/services-capabilities-sample/htorr

NDRI’s Human Tissue and Organs for Research Resource (HTORR) Program has been funded by the National Institutes of Health (NIH) for over 30 consecutive years to support research programs across multiple disciplines. It is through the HTORR program that NDRI provides academic biomedical investigators with donated normal and diseased human tissues and organs recovered from a diverse donor pool using customized procurement, processing, and preservation and distribution protocols. Our HTORR Program supports academic biomedical research investigators needs by providing: Access to a wide array of human biospecimens from any body system * Customized procurement in a variety of preservation formats including fresh, frozen, and fixed suitable for various analytical techniques * Reduced costs for tissue procurement * Technical support to design your studies utilizing human biospecimens * Letters of support and budgetary information for grant applications

Proper citation: Human Tissue and Organ for Research Resource (HTORR) (RRID:SCR_002859) Copy   

•   Source: SciCrunch Registry

http://dirline.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 15, 2016. Database containing location and descriptive information about a wide variety of information resources including organizations, research resources, projects, and databases concerned with health and biomedicine. This information may not be readily available in bibliographic databases. Each record may contain information on the publications, holdings, and services provided. These information resources fall into many categories including federal, state, and local government agencies; information and referral centers; professional societies; self-help groups and voluntary associations; academic and research institutions and their programs; information systems and research facilities. Topics include HIV/AIDS, maternal and child health, most diseases and conditions including genetic and other rare diseases, health services research and technology assessment. DIRLINE can be searched using subject words (such as disease or condition) including Medical Subject Headings (MeSH) or for the name or location of a resource. It now offers an A to Z list of over 8,500 organizations.

Proper citation: Directory of Health Organizations Online (RRID:SCR_002331) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/primate_atlas/

Symmetric atlas of the primate brain created using 18 cases of rhesus macaques aged 16-34 months. It includes the T1-weighted image (with and without skull), and also tissue segmentation probability maps (white matter, gray matter, CSF, rest), subcortical structures segmentation (amygdala, caudate, hippocampus, pallidus, putamen), and a lobar parcellation map. You can find more details about the creation of this atlas in the following paper : M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. In Proc SPIE Vol 6512, Medical Imaging, 2007, pp. 65122 L1-8

Proper citation: UNC Primate Brain Atlas (RRID:SCR_002570) Copy   

•   Source: SciCrunch Registry

http://eggnog.embl.de

A database of orthologous groups of genes. The orthologous groups are annotated with functional description lines (derived by identifying a common denominator for the genes based on their various annotations), with functional categories (i.e derived from the original COG/KOG categories). eggNOG's database currently counts 1.7 million orthologous groups in 3686 species, covering over 7.7 million proteins (built from 9.6 million proteins). (Jan 30, 2014)

Proper citation: eggNOG (RRID:SCR_002456) Copy   

•   Source: SciCrunch Registry

http://bioinf-apache.charite.de/supertarget_v2/

Database for analyzing drug-target interactions, it integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present (May 2013), the updated database contains >6000 target proteins, which are annotated with >330 000 relations to 196 000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range.

Proper citation: SuperTarget (RRID:SCR_002696) Copy   

•   Source: SciCrunch Registry