Biology subjects

Bridge, A.

Publications and source records attributed to Bridge, A..

3 recordsLinked to original sources

bioRxiv2019

Enzyme annotation in UniProtKB using Rhea

MotivationTo provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ontology ChEBI (Chemical Entities of Biological Interest).\n\nResultsWe replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API, and SPARQL endpoint that leverage the chemical structure data, nomenclature, and classification that Rhea and ChEBI provide.\n\nAvailability and ImplementationUniProtKB at https://www.uniprot.org/; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/sparql; Rhea at https://www.rhea-db.org/.\n\nContactanne.morgat@sib.swiss

bioinformatics↗

bioRxiv2019

HAMAP rules as SPARQL - A portable annotation pipeline for genomes and proteomes

MotivationGenome and proteome annotation pipelines are generally custom built and therefore not easily reusable by other groups, which leads to duplication of effort, increased costs, and suboptimal results. One cost-effective way to increase the data quality in public databases is to encourage the adoption of annotation standards and technological solutions that enable the sharing of biological knowledge and tools for genome and proteome annotation.\n\nResultsWe have translated the rules of our HAMAP proteome annotation pipeline to queries in the W3C standard SPARQL 1.1 syntax and applied them with two off-the-shelf SPARQL engines to UniProtKB/Swiss-Prot protein sequences described in RDF format. This approach is applicable to any genome or proteome annotation pipeline and greatly simplifies their reuse.\n\nAvailabilityHAMAP SPARQL rules and documentation are freely available for download from the HAMAP FTP site ftp://ftp.expasy.org/databases/hamap/hamapsparql.tar.gz under a CC-BY-ND 4.0 license. The annotations generated by the rules are under the CC-BY 4.0 license.\n\nContacthamap@sib.swiss\n\nSupplementary informationSupplementary data are included at the end of this document.

bioinformatics↗

bioRxiv2018

Navigating in vitro bioactivity data: investigating available resources using model compounds

Modern medicine and an increasingly complex environment contribute to exposure of humans to a large number of chemical compounds, that can potentially be toxic. Although widely used, compound testing in animals has important limitations. In vitro testing provides a promising alternative. However, because of the relative inaccessibility and fragmentation of available data, the in vitro approach largely underperforms its potential. The aim of this study is to investigate how available public online resources (tools and databases) support accessing and distribution of in vitro compound data. We examined 19 public online resources, mapped their features, and evaluated their usability with a set of four model compounds (aspirin, rosiglitazone, valproic acid, and tamoxifen). By investigating compound names and identifiers, we observed extensive variation and inconsistencies in available resources: the synonyms were different, compounds structural identifiers (InChI, InChIKey, SMILES and IUPAC systematic name) underperformed in omics databases, identification of compound related metadata (e.g. concentrations used in the experiments) from omics experiments was complex and none of the available resources clearly distinguished between in vivo and in vitro data. In addition, we estimated accessibility of selected public resources using computational queries. Only a few public resources provided access to compound-related data using semantic web technology. The general quality of experiment annotations created further difficulties in identifying data of interest. Therefore, we identified several standardized ontologies with potential to provide an increased accuracy for extensive data retrieval of in vitro compound data. Furthermore, using the examples of our model compounds, we provide recommendations on the use of ontologies by suggesting specific ontology terms to annotate in vitro experimental data when being published.

pharmacology and toxicology↗