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http://prism.ccbb.ku.edu.tr/hotregion/index.php
Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. HotRegion provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. The number of interfaces in the database is 147909 and still growing.
Proper citation: HotRegion - A Database of Cooperative Hotspots (RRID:SCR_006022) Copy
GlycomeDB is a database of all known carbohydrate structures. This was achieved by crosslinking several other databases of carbohydrate structures by using the GlycoCT XML language specification. We have analyzed all of the existing public databases and defined a sequence format based on XML (GlycoCT) capable of storing all structural information of carbohydrate sequences. We have implemented a library of parsers for the interpretation of the different encoding schemes for carbohydrates. With this library we have translated the carbohydrate sequences of all freely available databases (CFG , KEGG, GLYCOSCIENCES.de, BCSDB and Carbbank) to GlycoCT, and created a new database (GlycomeDB) containing all structures and annotations. During the process of data integration we found multiple inconsistencies in the existing databases which were corrected in collaboration with the responsible curators. With the new database, GlycomeDB, it is possible to get an overview of all carbohydrate structures in the different databases and to crosslink common structures in the different databases. Scientists are now able to search for a particular structure in the meta database and get information about the occurrence of this structure in the five carbohydrate structure databases.
Proper citation: glycomedb (RRID:SCR_005717) Copy
http://tropgenedb.cirad.fr/tropgene/JSP/index.jsp
A database that manages genetic and genomic information about tropical crops studied by Cirad. The database is organised into crop specific modules. Each module includes data on genetic ressources (agro-morphological data, parentages, allelic diversity), information on molecular markers, genetics maps, result of QTL analyses, data from physical mapping, sequences, genes, as well as corresponding references. GENE DB interface has been designed to allow quick consultations as well as complex queries. Nine modules are presently on line.
Proper citation: TropGENE DB (RRID:SCR_005716) Copy
DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.
Proper citation: DOMMINO - Database Of MacroMolecular INteractiOns (RRID:SCR_005958) Copy
http://www.jcvi.org/charprotdb/index.cgi/home
The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.
Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy
http://pbildb1.univ-lyon1.fr/virhostnet/
Public knowledge base specialized in the management and analysis of integrated virus-virus, virus-host and host-host interaction networks coupled to their functional annotations. It contains high quality and up-to-date information gathered and curated from public databases (VirusMint, Intact, HIV-1 database). It allows users to search by host gene, host/viral protein, gene ontology function, KEGG pathway, Interpro domain, and publication information. It also allows users to browse viral taxonomy.
Proper citation: VirHostNet: Virus-Host Network (RRID:SCR_005978) Copy
https://gitlab.com/kyrgyzov/lsa_slurm
Software tool to implement pre-assembly binning scheme leveraging sparse dictionary learning and matrix factorization to solve sparse decomposition problems arising in field of metagenomics.
Proper citation: lsa_slurm (RRID:SCR_018134) Copy
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