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http://tiger.dbs.nus.edu.sg/cnv-seq/

A method for detecting DNA copy number variation (CNV) using high-throughput sequencing., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CNV-seq (RRID:SCR_013357) Copy   

•   Source: SciCrunch Registry

http://diseases.jensenlab.org/

Database that integrates evidence on disease-gene associations from automatic text mining, manually curated literature, cancer mutation data, and genome-wide association studies. It also assigns confidence scores that facilitate comparison of the different types and sources of evidence.

Proper citation: DISEASES (RRID:SCR_015664) Copy   

•   Source: SciCrunch Registry

http://pallab.serc.iisc.ernet.in/gester/

Database of intrinsic terminators of transcription that is comprized of >2,200,000 bacterial terminators identified from a total of 2036 chromosomes and 1508 plasmids. Information about structural parameters of individual terminators such as sequence, length of stem and loop, mismatches and gaps, U-trail, genomic coordinates and gene name and accession number is available in both tabular form and as a composite figure. Summary statistics for terminator profiles of whole genome can be also obtained. Raw data files for individual genomes can be downloaded (.zip files) for detailed investigations. Data is organized into different tiers such that users can fine-tune their search by entering name of the species, or taxon ID or genomes with a certain number of terminators. To visualize the occurrence of the terminators, an interactive map, with the resolution to single gene level, has been developed.

Proper citation: WebGeSTer DB (RRID:SCR_002165) Copy   

•   Source: SciCrunch Registry

http://www.pubgene.org/

It helps users retrieve information on genes and proteins. The underlying structure of PubGene can be viewed as a gene-centric database. Gene and protein names are cross-referenced to each other and to terms that are relevant to understanding their biological function, importance in disease and relationship to chemical substances. The result is a literature network organizing information in a form that is easy to navigate.

Proper citation: PubGene (RRID:SCR_002119) Copy   

•   Source: SciCrunch Registry

http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) 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://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://ndbserver.rutgers.edu/

A database of three-dimensional structural information about nucleic acids and their complexes. In addition to primary data, it contains derived geometric data, classifications of structures and motifs, standards for describing nucleic acid features, as well as tools and software for the analysis of nucleic acids. A variety of search capabilities are available, as are many different types of reports. NDB maintains the macromolecular Crystallographic Information File (mmCIF).

Proper citation: Nucleic Acid Database (RRID:SCR_003255) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/RefSeq/

Collection of curated, non-redundant genomic DNA, transcript RNA, and protein sequences produced by NCBI. Provides a reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. Accessed through the Nucleotide and Protein databases.

Proper citation: RefSeq (RRID:SCR_003496) Copy   

•   Source: SciCrunch Registry

http://mutdb.org/

Database with annotations for human variation data with protein structural information and other functionally relevant information, if available. The mutations are organized by gene.

Proper citation: MutDB (RRID:SCR_003251) Copy   

•   Source: SciCrunch Registry

http://compbio.uthsc.edu/miRSNP/

Database of naturally occurring DNA variations in microRNA (miRNA) seed regions and miRNA target sites. MicroRNAs pair to the transcripts of protein-coding genes and cause translational repression or mRNA destabilization. SNPs and INDELs in miRNAs and their target sites may affect miRNA-mRNA interaction, and hence affect miRNA-mediated gene repression. The PolymiRTS database was created by scanning 3'UTRs of mRNAs in human and mouse for SNPs and INDELs in miRNA target sites. Then, the potential downstream effects of these polymorphisms on gene expression and higher-order phenotypes are identified. Specifically, genes containing PolymiRTSs, cis-acting expression QTLs, and physiological QTLs in mouse and the results of genome-wide association studies (GWAS) of human traits and diseases are linked in the database. The PolymiRTS database also includes polymorphisms in target sites that have been supported by a variety of experimental methods and polymorphisms in miRNA seed regions.

Proper citation: PolymiRTS (RRID:SCR_003389) Copy   

•   Source: SciCrunch Registry

http://www.phi-base.org/

Database that catalogs experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. It is an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, it also includes antifungal compounds and their target genes. Each entry is curated by domain experts and is supported by strong experimental evidence (gene disruption experiments, STM etc), as well as literature references in which the original experiments are described. Each gene is presented with its nucleotide and deduced amino acid sequence, as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes have been annotated using controlled vocabularies and links to external sources (Gene Ontology terms, EC Numbers, NCBI taxonomy, EMBL, PubMed and FRAC).

Proper citation: PHI-base (RRID:SCR_003331) Copy   

•   Source: SciCrunch Registry

http://mistdb.com

Database which contains the signal transduction proteins for complete and draft bacterial and archaeal genomes. The MiST2 database identifies and catalogs the repertoire of signal transduction proteins in microbial genomes.

Proper citation: MiST - Microbial Signal Transduction database (RRID:SCR_003166) Copy   

•   Source: SciCrunch Registry

http://sideeffects.embl.de/

Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.

Proper citation: SIDER (RRID:SCR_004321) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/taxonomy/

NEWT is the taxonomy database maintained by the UniProt group. It integrates taxonomy data compiled in the NCBI database and data specific to the UniProt Knowledgebase. Browse by hierarchy, List all, or Complete proteomes. Organisms are classified in a hierarchical tree structure. Our taxonomy database contains every node (taxon) of the tree. UniProtKB taxonomy data is manually curated: next to manually verified organism names, we provide a selection of external links, organism strains and viral host information. Species with protein sequences stored in the UniProt Knowledgebase are named according to UniProt nomenclature. We endeavour to maintain a list of manually curated species names for which protein sequence data is available. In particular, we have adopted a systematic convention for naming viral and bacterial strains and isolates. Links to external sites are chosen by the UniProt taxonomy team and show pictures and various scientific data of interest (taxonomy, biology, physiology,...).

Proper citation: NEWT (RRID:SCR_004477) Copy   

•   Source: SciCrunch Registry

http://selectome.unil.ch/

Database of positive selection based on a rigorous branch-site specific likelihood test. Positive selection is detected using CODEML on all branches of animal gene trees.

Proper citation: Selectome: a Database of Positive Selection (RRID:SCR_004542) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.org/go2msig/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 24, 2020. Software tool as automated Gene Ontology based multi species gene set generator for gene set enrichment analysis. Used to generate gene sets required for Gene Set Enrichment Analysis for almost any organism for which GO term association data exists.
Gene set collections can be automatically created for wide variety of species.

Proper citation: GO2MSIG (RRID:SCR_018359) Copy   

•   Source: SciCrunch Registry