Tools   Select Another Resource Report Type

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   

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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   

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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   

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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   

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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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http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

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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   

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http://www.gene-regulation.com/pub/databases.html#transfac

Manually curated database of eukaryotic transcription factors, their genomic binding sites and DNA binding profiles. Used to predict potential transcription factor binding sites.

Proper citation: TRANSFAC (RRID:SCR_005620) Copy   

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http://indel.bioinfo.sdu.edu.cn/gridsphere/gridsphere

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Indel Flanking Region Database is an online resource for indels and the flanking regions of proteins in SCOP superfamilies, including amino acid sequences, lengths, locations, secondary structure constitutions, hydrophilicity / hydrophobicity, domain information, 3D structures and so on. It aims at providing a comprehensive dataset for analyzing the qualities of amino acid insertion/deletions(indels), substitutions and the relationship between them. The indels were obtained through the pairwise alignment of homologous structures in SCOP superfamilies. The IndelFR database contains 2,925,017 indels with flanking regions extracted from 373,402 structural alignment pairs of 12,573 non-redundant domains from 1053 superfamilies. IndelFR has already been used for molecular evolution studies and may help to promote future functional studies of indels and their flanking regions.

Proper citation: IndelFR - Indel Flanking Region Database (RRID:SCR_006050) Copy   

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http://www.snpedia.com/index.php/SNPedia

Wiki investigating human genetics including information about the effects of variations in DNA, citing peer-reviewed scientific publications. It is used by Promethease to analyze and help explain your DNA. It is based on a wiki model in order to foster communication about genetic variation and to allow interested community members to help it evolve to become ever more relevant. As the cost of genotyping (and especially of fully determining your own genomic sequence) continues to drop, we''''ll all want to know more - a lot more - about the meaning of these DNA variations and SNPedia will be here to help. SNPedia has been launched to help realize the potential of the Human Genome Project to connect to our daily lives and well-being. For more information see the Wikipedia page, http://en.wikipedia.org/wiki/SNPedia * Download URL: http://www.SNPedia.com/index.php/Bulk * Web Service URL: http://bots.SNPedia.com/api.php

Proper citation: SNPedia (RRID:SCR_006125) Copy   

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http://cossmos.slu.edu/

Database to search through the nucleic acid structures from the Protein Data Bank and examine structural motifs, including (a)symmetric internal loops, bulge loops, and hairpin loops. They have compiled over 2,000 three-dimensional structures, which can now be searched using different parameters, including PDB information, experimental technique, sequence, and motif type. RNA secondary structure is important for designing therapeutics, understanding protein-RNA binding and predicting tertiary structure of RNA. Several databases and downloadable programs exist that specialize in the three-dimensional (3D) structure of RNA, but none focus specifically on secondary structural motifs such as internal, bulge and hairpin loops. To create the RNA CoSSMos database, 2156 Protein Data Bank (PDB) files were searched for internal, bulge and hairpin loops, and each loop''''s structural information, including sugar pucker, glycosidic linkage, hydrogen bonding patterns and stacking interactions, was included in the database. False positives were defined, identified and reclassified or omitted from the database to ensure the most accurate results possible. Users can search via general PDB information, experimental parameters, sequence and specific motif and by specific structural parameters in the subquery page after the initial search. Returned results for each search can be viewed individually or a complete set can be downloaded into a spreadsheet to allow for easy comparison. The RNA CoSSMos database is updated weekly.

Proper citation: RNA CoSSMos (RRID:SCR_006120) Copy   

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http://nmr.cmbi.ru.nl/NRG-CING/HTML/index.html

NRG-CING presents a complete validation report for all 9,000+ wwPDB NMR entries including remediated experimental data such as chemical shifts from BMRB and restraints from NRG . These CING reports are compiled from internal analyses and those by CCPN, DSSP, PROCHECK-NMR/Aqua, ShiftX, Talos+, Vasco, Wattos, and WHAT_CHECK. The NRG-CING website is a collection of CING reports that has been pre-calculated for all PDB files solved by NMR. (See website for more information on CING.) In case the underlying experimental data is available, these have been cleaned up and made syntactically and semantically correct and homogeneous. For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists used in the structure calculation are accessible, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. Assessment of the quality of the structural result is paramount to their usage and a combined, integrated repository of both input data and structural results greatly facilitates such an analysis. In addition, the accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculations using the available experimental data and present-day software with improved protocols and force fields. Such efforts, however, generally fail on over half of all deposited structures due to the syntactic and semantic heterogeneity of the data and the wide variety of formats used for their deposition. We have combined the cleaned-up restraints information from the NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank in the weekly updated NRG-CING database. Eleven programs, in addition to CING itself, have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available restraint and chemical shift data. The longitudinal validation of this data yielded a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The cleaned up NMR experimental datasets and the validation reports are freely available.

Proper citation: NRG-CING (RRID:SCR_006079) Copy   

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http://proportal.mit.edu/

ProPortal is a database containing genomic, metagenomic, transcriptomic and field data for the marine cyanobacterium Prochlorococcus. Our goal is to provide a source of cross-referenced data across multiple scales of biological organization--from the genome to the ecosystem--embracing the full diversity of ecotypic variation within this microbial taxon, its sister group, Synechococcus and phage that infect them. The site currently contains the genomes of 13 Prochlorococcus strains, 11 Synechococcus strains and 28 cyanophage strains that infect one or both groups. Cyanobacterial and cyanophage genes are clustered into orthologous groups that can be accessed by keyword search or through a genome browser. Users can also identify orthologous gene clusters shared by cyanobacterial and cyanophage genomes. Gene expression data for Prochlorococcus ecotypes MED4 and MIT9313 allow users to identify genes that are up or downregulated in response to environmental stressors. In addition, the transcriptome in synchronized cells grown on a 24-h light-dark cycle reveals the choreography of gene expression in cells in a ''natural'' state. Metagenomic sequences from the Global Ocean Survey from Prochlorococcus, Synechococcus and phage genomes are archived so users can examine the differences between populations from diverse habitats. Finally, an example of cyanobacterial population data from the field is included.

Proper citation: ProPortal (RRID:SCR_006112) Copy   

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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.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://mint.bio.uniroma2.it/virusmint/

A virus protein interactions database that collects and annotates all the interactions between human and viral proteins and integrates this information in the human protein interaction network. It uses the PSI-MI standard and is fully integrated with the MINT database. You can search for any viral or human protein by entering either common names or database identifiers or display a complete viral interactome.

Proper citation: VirusMINT (RRID:SCR_005987) Copy   

•   Source: SciCrunch Registry

http://www.bioinsilico.org/cgi-bin/CAPSDB/staticHTML/home

It is a structural classification of helix-cappings or caps compiled from protein structures. Caps extracted from protein structures have been structurally classified based on geometry and conformation and organized in a tree-like hierarchical classification where the different levels correspond to different properties of the caps. CASP-DB is fully browsable and searchable and is regularly updated. The regions of the polypeptide chain immediately preceding or following a helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. CapsDB v4.0 * PDB structures: 4591 * Number of clusters: 859 * Number of caps: 31452

Proper citation: CAPS Database (RRID:SCR_006862) Copy   

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http://scop.mrc-lmb.cam.ac.uk/scop/

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases.

Proper citation: SCOP: Structural Classification of Proteins (RRID:SCR_007039) Copy   

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https://myhits.sib.swiss/

Database devoted to protein domains. It is also a collection of tools for the investigation of the relationships between protein sequences and motifs described on them.

Proper citation: MyHits (RRID:SCR_006757) Copy   

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http://bioinformatics.biol.uoa.gr/cuticleDB

A relational database containing all structural proteins of Arthropod cuticle identified to date. Many come from direct sequencing of proteins isolated from cuticle and from sequences from cDNAs that share common features with these authentic cuticular proteins. It also includes proteins from the five sequenced genomes where manual annotation has been applied to cuticular proteins: Anopheles gambiae, Apis mellifera, Bombyx mori, Drosophila melanogaster, and Nasonia vitripennis. Some sequences were confirmed as authentic cuticular proteins because protein sequencing revealed that they were present in cuticle; others were identified by sequence homology and other criteria. Entries provides information about whether sequences are putative or authentic cuticular proteins. CuticleDB was primarily designed to contain correct and full annotation of cuticular protein data. The database will be of help to future genome annotators. Users will be able to test hypotheses for the existence of known and also of yet unknown motifs in cuticular proteins. An analysis of motifs may contribute to understanding how proteins contribute to the physical properties of cuticle as well as to the precise nature of their interaction with chitin.

Proper citation: CuticleDB (RRID:SCR_007045) Copy   

•   Source: SciCrunch Registry