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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
An interactive web server that enables researchers to prioritize any list of genes by their biological proximity to defined core genes (i.e. genes that are known to be associated with the phenotype), and to predict novel gene pathways.
Proper citation: Human Gene Connectome Server (RRID:SCR_002627) Copy
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
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
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
http://webdocs.cs.ualberta.ca/~bioinfo/PA/Sub/
Web server specialized to predict the subcellular localization of proteins using established machine learning techniques.
Proper citation: Proteome Analyst Specialized Subcellular Localization Server (RRID:SCR_003143) Copy
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
http://nar.oxfordjournals.org/content/34/suppl_2/W635.long
THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 9, 2016. A web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by deploying more Web wrapper agents. FASTSNP provides three options for users to submit requests. If users already have some candidate SNPs on a candidate gene, they may use Query by Candidate Gene to select the specific SNPs on the gene to perform prioritization. If users have a specified SNP or a list of SNP rsid's needs to be prioritized, they can use Query by SNP option and upload the SNP list in an Excel-format file. Finally, if users have a novel SNP sequence, FASTSNP provides Novel SNP analysis. FASTSNP will generate a SNP Function Report for each SNP. Users can export SNP data to an excel file for further genotyping processes. Other features of FASTSNP include SNP quality checking and haplotype LD information.
Proper citation: FastSNP (RRID:SCR_003140) Copy
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
One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool
Proper citation: T-profiler (RRID:SCR_003452) Copy
Web server based on the Enhancer Identification (EI) method, to determine the chromosomal location and functional characteristics of distant regulatory elements (REs) in higher eukaryotic genomes. The server uses gene co-expression data, comparative genomics, and combinatorics of transcription factor binding sites (TFBSs) to find TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is the detection of REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function, or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs, and it also scores the association of individual TFs with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data.
Proper citation: Distant Regulatory Elements (RRID:SCR_003058) Copy
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
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
http://amphoranet.pitgroup.org/
Webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data. It is capable of assigning a probability-weighted taxonomic group for each phylogenetic marker gene found in the input metagenomic sample.
Proper citation: AmphoraNet (RRID:SCR_005009) Copy
Data analysis service that analyzes DNA sequences and determines their most likely phylogenetic origin. Its main use is in metagenomics projects, where DNA is isolated directly from natural environments and sequenced (the organisms from which the DNA originates are often entirely undescribed). It will search such sequences for suitable marker genes, and will use maximum likelihood analysis to place them in the ''''Tree of Life''''. This placement is more reliable than simply assessing the closest relative of a sequence using BLAST. More importantly, MLTreeMap decides not only who is the closest relative of your query sequence, but also how deep in the tree of life it probably branched off. Additionally, MLTreeMap searches the sequences for genes, which are coding for key enzymes of important functional pathways, such as RuBisCo, methane monooxygenase or nitrogenase. In case of a positive hit, MLTreeMap uses maximum likelihood analysis to place them in the respective ''''gene-family tree''''.
Proper citation: MLTreeMap (RRID:SCR_004792) Copy
http://www.ebi.ac.uk/biosamples/
Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)
Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy
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
A web-based tool to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner.
Proper citation: INMEX (RRID:SCR_004173) Copy
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
http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi
A web server and database that organizes, analyzes and predicts interactions between proteins and other biomolecules. For a given protein sequence or structure query, it reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in experimentally-determined structural biological assemblies. It also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.
Proper citation: IBIS: Inferred Biomolecular Interactions Server (RRID:SCR_004886) Copy
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