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http://www.uniprot.org/uniparc/

Database that contains publicly available protein sequences with stable and unique identifiers (UPI) which are never removed, changed or reassigned. UniParc tracks sequence changes in the source databases and archives the history of all changes. Information other than protein sequence must be retrieved from the UniParc source databases using the database cross-references.

Proper citation: UniParc (RRID:SCR_005818) Copy   

•   Source: SciCrunch Registry

http://trbase.ex.ac.uk/

This TRbase is a relational tandem repeat database that relates tandem repeats to gene locations and disease genes of the human genome. The TRbase stores both perfect and imperfect repeats of 1 to 2000 bp unit lengths that were identified using the Tandem Repeat Finder program. Disease information for all 24 chromosomes was retrieved from the Online Mendelian Inheritance in Man (OMIM) database. There are five main search forms by which the user may query the database: 1. The Advanced tandem repeat search: This allows a complete search for tandem repeats using a combination of criteria, such as total tandem repeat length, repeat unit length, copy number of the repeats, percentage matches and the consensus repeat pattern. On submission, the number of repeats and the detailed tandem repeat characteristics of each repeat that match the user query are tabulated. 2. The Main search: This relates tandem repeat data to genes and diseases. The user may specify a gene of interest to view details of all repeats associated with it or search for tandem repeats present in a particular disease by entering the name/keyword for the disease or the MIM number of the disease gene. 3. The Composite search: This more advanced search allows the user to query specifically for repeats present in exons, introns or intergenic regions of a gene or disease gene. 4. The Gene Search: Further information on genes can be available by a simple gene name search on this page. 5. The Disease search: This allows extensive information on disease genes on all chromosomes of the human genome. Searching for a MIM number, or keyword searches specifying the features of the disease, will retrieve the information on the disease and the chromosome in which the disease gene occurs. Each entry retrieved is linked to the OMIM database for detailed literature and gene map information on the disease.

Proper citation: TRbase: A Database Of Tandem Repeats In The Human Genome (RRID:SCR_005658) Copy   

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http://possum.cbrc.jp/PoSSuM/

Relational database of all the discovered similar pairs in a huge number of protein-ligand binding sites with annotations of various types (e.g., CATH, SCOP, EC number, Gene ontology). They used a tremendously fast algorithm called SketchSort that enables the enumeration of similar pairs in a huge number of protein-ligand binding sites. They conducted all-pair similarity searches for 3.4 million known and potential binding sites using the proposed method and discovered over 24 million similar pairs of binding sites. PoSSuM enables rapid exploration of similar binding sites among structures with different global folds as well as similar ones. Moreover, PoSSuM is useful for predicting the binding ligand for unbound structures. Basically, the users can search similar binding pockets using two search modes: # Search K is useful for finding similar binding sites for a known ligand-binding site. Post a known ligand-binding site (a pair of PDB ID and HET code) in the PDB, and PoSSuM will search similar sites for the query site. # Search P is useful for predicting ligands that potentially bind to a structure of interest. Post a known protein structure (PDB ID) in the PDB, and PoSSuM will search similar known-ligand binding sites for the query structure.

Proper citation: PoSSuM (RRID:SCR_006109) Copy   

•   Source: SciCrunch Registry

http://tandem.bu.edu/cgi-bin/trdb/trdb.exe

A public repository of information on tandem repeats in genomic DNA and contains a variety of tools for their analysis. These currently include the Tandem Repeats Finder algorithm, query and filtering capabilities for finding particular repeats of interest, repeat clustering algorithms based on sequence similarity, polymorphism prediction based on common patterns of mutation, PCR primer selection, and data download in a variety of formats. In addition, TRDB serves as a centralized research workbench, provides storage space for results of analysis, and permits collaborators to privately share their data and analysis.

Proper citation: Tandem Repeats Database (RRID:SCR_005659) Copy   

•   Source: SciCrunch Registry

http://lussierlab.org/GO-Module/GOModule.cgi

GO-Module provides an interface to reduce the dimensionality of GO enrichment results and produce interpretable biomodules of significant GO terms organized by hierarchical knowledge that contain only true positive results. Users can download a text file of GO terms annotated with their significance and identified biomodules, a network visualization of resultant GO IDs or terms in PDF format, and view results in an online table. Platform: Online tool

Proper citation: GO-Module (RRID:SCR_005813) Copy   

•   Source: SciCrunch Registry

http://opencitations.net/

Database of biomedical literature citations, harvested from the reference lists of all open access articles in PubMed Central that reference ~20% of all PubMed Central papers (approx. 3.4 million papers), including all the highly cited papers in every biomedical field. All the data are freely available for download and reuse. The web site allows these bibliographic records and citations to be browsed, individual articles to be selected, and its citation network to be visualized in a variety of displays. Details of each selected reference, and the data and diagrams for its citation network, may be downloaded in a variety of formats, while the entire Open Citations Corpus can be downloaded from our source data page in several formats including RDF and BibJSON. Their aim for the future is to work with publishers to make available the reference lists from many more current and recent journal articles, starting with the biomedical literature, and to make the citations contained within them available as Open Linked Data in the manner demonstrated by the existing exemplar data available here.

Proper citation: JISC Open Citations (RRID:SCR_005936) Copy   

•   Source: SciCrunch Registry

http://worfdb.dfci.harvard.edu/

Database that integrates and disseminates the data from the cloning of complete set of predicted protein-encoding ORFs of Caenorhabditis elegans. It also allows the community to search for availability and quality of cloned ORFs. So far, ORF sequence tags (OSTs) obtained for all individual clones have allowed exon structure corrections for ORFs originally predicted by the C. elegans sequencing consortium. The database contains this OST information along with data pertinent to the cloning process.

Proper citation: WorfDB (RRID:SCR_006028) Copy   

•   Source: SciCrunch Registry

http://www.bionet.nsc.ru/trrd/

TRRD is a unique information resource, accumulating information on structural and functional organization of transcription regulatory regions of eukaryotic genes. Only experimentally confirmed information is included into TRRD. Transcription Regulatory Regions Database (TRRD) is developed for accumulation of experimental information on the structure-function features of regulatory regions of eukaryotic genes. Each entry of TRRD corresponds to a particular gene. The annotated part of an entry includes the structure-function description of gene regulatory regions composed by regulatory units (promoters, silencers, enhancers, etc.), individual transcription factor binding sites that constitute these regulatory units, and transcription factors that bind to these sites. In addition, the entry contains the gene expression patterns and references to original publications.

Proper citation: Transcription Regulatory Regions Database (RRID:SCR_005723) Copy   

•   Source: SciCrunch Registry

http://202.120.189.88/drvis/

Dr.VIS collects and locates human disease-related viral integration sites. So far, about 600 sites covering 5 virus organisms and 11 human diseases are available. Integration sites in Dr.VIS are located against chromosome, cytoband, gene and refseq position as specific as possible. Viral-cellular junction sequences are extracted from papers and nucleotide databases, and linked to corresponding integration sites Graphic views summarizing distribution of viral integration sites are generated according to chromosome maps. Dr.VIS is built with a hope to facilitate research of human diseases and viruses. Dr.VIS provides curated knowledge of integration sites from chromosome region narrow to genomic position, as well as junction sequences if available. Dr.VIS is an open resource for free.

Proper citation: Dr.VIS - Human Disease-Related Viral Integration Sites (RRID:SCR_005965) Copy   

•   Source: SciCrunch Registry

http://www.agbase.msstate.edu/cgi-bin/tools/GOanna.cgi

GOanna is used to find annotations for proteins using a similarity search. The input can be a list of IDs or it can be a list of sequences in FASTA format. GOanna will retrieve the sequences if necessary and conduct the specified BLAST search against a user-specified database of GO annotated proteins. The resulting file contains GO annotations of the top BLAST hits. The sequence alignments are also provided so the user can use these to access the quality of the match. Platform: Online tool

Proper citation: GOanna (RRID:SCR_005684) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/FunTree/

FunTree provides a range of data resources to detect the evolution of enzyme function within distant structurally related clusters within domain super families as determined by CATH. To access the resource enter a specific CATH superfamily code or search for a structure / sequence / function (either via a EC code or KEGG ligand / reaction ID, PDB ID or UniProtKB ID). Or browse the resource via superfamily / function / structure / metabolites & reactions via the menu on the left panel. FunTree is a new resource that brings together sequence, structure, phylogenetic, chemical and mechanistic information for structurally defined enzyme superfamilies. Gathering together this range of data into a single resource allows the investigation of how novel enzyme functions have evolved within a structurally defined superfamily as well as providing a means to analyse trends across many superfamilies. This is done not only within the context of an enzyme''''s sequence and structure but also the relationships of their reactions. Developed in tandem with the CATH database, it currently comprises 276 superfamilies covering 1800 (70%) of sequence assigned enzyme reactions. Central to the resource are phylogenetic trees generated from structurally informed multiple sequence alignments using both domain structural alignments supplemented with domain sequences and whole sequence alignments based on commonality of multi-domain architectures. These trees are decorated with functional annotations such as metabolite similarity as well as annotations from manually curated resources such the catalytic site atlas and MACiE for enzyme mechanisms.

Proper citation: FunTree (RRID:SCR_006014) Copy   

•   Source: SciCrunch Registry

http://www.proquest.com/en-US/

Service that helps users navigate the research journey, connecting people and information from dissertations to governmental and cultural archives to news, in all its forms. Its role is essential to libraries and other organizations whose missions depend on the delivery of complete, trustworthy information. ProQuest''s massive information pool, built through partnerships with content creators, is navigated through technological innovations that enable users to quickly find just the right information. The ProQuest platform moves beyond navigation to empower researchers to use, create, and share contentaccelerating research productivity. The Summon web-scale discovery service is a boon to academic libraries worldwide. ProQuest expanded into corporate and government markets, with the ProQuest Dialog service and acquiring Congressional Information Services and University Publications of America. It acquired ebrary, expanding ProQuest''s content base to include e-books and adding to the technology expertise resident across the enterprise, which also includes such units as Serials Solutions, RefWorks-COS, and Bowker.

Proper citation: ProQuest (RRID:SCR_006093) Copy   

•   Source: SciCrunch Registry

http://vita.mbc.nctu.edu.tw/

A database which collects virus data from miRBase and ICTV, VirGne, VBRC., etc, including known viral miRNAs and supporting predicted host miRNA targets by miRanda and TargetScan. ViTa also provides effective annotations, including human miRNA expression, virus infected tissues, annotation of virus and comparisons. Additionally, multiple functions and graphical web interface are designed and implemented to help users to investigate the microRNA roles in viral existence., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ViTa- Virus microRNA Target (RRID:SCR_005955) Copy   

•   Source: SciCrunch Registry

http://ygac.med.yale.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. TRIPLES provides full public access to the data and reagents generated from ongoing functional analysis of the yeast genome. Using a novel transposon-tagging approach, we have analyzed disruption phenotypes, gene expression, and protein localization on a genome-wide scale in Saccharomyces. The data generated from this study may be accessed through our database, TRIPLES ; additionally, all reagents generated in this study are freely available from on-line order forms (linked to TRIPLES as well). multipurpose, mini-transposon, mutant alleles, phenotypes, protein localization, gene expression, Saccharomyces cerevisiae, Web-accessible database, transposon-mutagenized yeast strains, downloaded, tab-delimited, text file, protein localization data, fluorescent micrographs, staining patterns, indirect immunofluorescence analysis of indicated epitope-tagged proteins, subcellular localization of the yeast proteome, visual library, Nucleic Acid Sequence Data Library (GenBank), clone report, graphic map, transposon insertions (represented as flags)

Proper citation: TRIPLES- a database of TRansposon-Insertion Phenotypes Localization and Expression in Saccharomyces (RRID:SCR_005714) Copy   

•   Source: SciCrunch Registry

https://www.clinicaltrialsregister.eu

Database of European clinical trials containing information on interventional clinical trials on medicines. The information available dates from 1 May 2004 when national medicine regulatory authorities began populating the EudraCT database, the application that is used by national medicine regulatory authorities to enter clinical trial data. The EU Clinical Trials Register website launched on 22 March 2011 enables users to search for information which has been included in the EudraCT database. Users are able to: * view the description of a phase II-IV adult clinical trial where the investigator sites are in European Union member states and the European Economic Area; * view the description of any pediatric clinical trial with investigator sites in the European Union and any trials which form part of a pediatric investigation plan (PIP) including those where the investigator sites are outside the European Union. * download up to 20 results (per request) in a text file (.txt). The details in the clinical trial description include: * the design of the trial; * the sponsor; * the investigational medicine (trade name or active substance identification); * the therapeutic areas; * the status (authorized, ongoing, complete).

Proper citation: EU Clinical Trials Register (RRID:SCR_005956) Copy   

•   Source: SciCrunch Registry

https://www.facebase.org/facial_norms/

Database of high-quality craniofacial anthropometric normative data for the research and clinical community based on digital stereophotogrammetry. Unlike traditional craniofacial normative datasets that are limited to measures obtained with handheld calipers and tape measurers, the anthropometric data provided here are based on digital stereophotogrammetry, a method of 3D surface imaging ideally suited for capturing human facial surface morphology. Also unlike more traditional normative craniofacial resources, the 3D Facial Norms Database allows users to interact with data via an intuitive graphical interface and - given proper credentials - gain access to individual-level data, allowing users to perform their own analyses.

Proper citation: 3D Facial Norms Database (RRID:SCR_005991) Copy   

•   Source: SciCrunch Registry

http://omniBiomarker.bme.gatech.edu

omniBiomarker is a web-application for analysis of high-throughput -omic data. Its primary function is to identify differentially expressed biomarkers that may be used for diagnostic or prognostic clinical prediction. Currently, omniBiomarker allows users to analyze their data with many different ranking methods simultaneously using a high-performance compute cluster. The next release of omniBiomarker will automatically select the most biologically relevant ranking method based on user input regarding prior knowledge. The omniBiomarker workflow * Data: Gene Expression * Algorithms: Knowledge-Driven Gene Ranking * Differentially expressed Genes * Clinical / Biological Validation * Knowledge: NCI Thesaurus of Cancer, Cancer Gene Index * back to Algorithms

Proper citation: omniBiomarker (RRID:SCR_005750) Copy   

•   Source: SciCrunch Registry

http://igdb.nsclc.ibms.sinica.edu.tw/

IGDB.NSCLC database is aiming to facilitate and prioritize identified lung cancer genes and microRNAs for pathological and mechanistic studies of lung tumorigenesis and for developing new strategies for clinical interventions. We integrated and curated various lung cancer genomic datasets to present # lung cancer genes with somatic mutations, experimental supports and statistic significance in association with clinicopathological features; # genomic alterations with copy number alterations (CNA) detected by high density SNP arrays, gain or loss regions detected by arrayed comparative genome hybridization (aCGH), and loss of heterozygosity (LOH) detected by microsatellite markers; # aberrant expression of genes and microRNAs detected by various microarrays. IGDB.NSCLC database provides user friendly interfaces and searching functions to display multiple layers of evidence for detecting lung cancer target genes and microRNAs, especially emphasizing on concordant alterations: # genes with altered expression located in the CNA regions; # microRNAs with altered expression located in the CNA regions; # somatic mutation genes located in the CNA regions; and # genes associated with clinicopathological features located in the CNA regions. These concordant altered genes and miRNAs should be prioritized for further basic and clinical studies.

Proper citation: IGDB.NSCLC (RRID:SCR_006048) Copy   

•   Source: SciCrunch Registry

http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=31537&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 4, 2014. Tissue Biobank collects samples from different types of cancers patients prospectively. Blood samples are being sent to KI Biobank for DNA extraction and storage. Number of sample donors: 611 (June 2010)

Proper citation: KI Biobank - Tissue Biobank (RRID:SCR_006043) Copy   

•   Source: SciCrunch Registry

http://www.hpppi.iicb.res.in/btox/

Database of Bacterial ExoToxins for Human is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach).

Proper citation: DBETH - Database for Bacterial ExoToxins for Humans (RRID:SCR_005908) Copy   

•   Source: SciCrunch Registry