Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale.


Journal

IEEE/ACM transactions on computational biology and bioinformatics
ISSN: 1557-9964
Titre abrégé: IEEE/ACM Trans Comput Biol Bioinform
Pays: United States
ID NLM: 101196755

Informations de publication

Date de publication:
Historique:
pubmed: 3 9 2020
medline: 6 4 2022
entrez: 3 9 2020
Statut: ppublish

Résumé

The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing availability of genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address these challenges, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. In this paper, we describe Functional Genomics Platform (formerly known as OMXWare), a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, the Functional Genomics Platform today contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. The Functional Genomics Platform maps all of the many-to-many connections between each biological entity including the originating genome, gene, protein, and protein domain. Various microbial studies, from infectious disease to environmental health, can benefit from the rich data and connections. We describe the data selection, the pipeline to create and update the Functional Genomics Platform, and the developer tools (Python SDK and REST APIs)which allow researchers to efficiently study microbial life at scale.

Identifiants

pubmed: 32877338
doi: 10.1109/TCBB.2020.3021231
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

940-952

Auteurs

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Classifications MeSH