ePath: an online database towards comprehensive essential gene annotation for prokaryotes.
Sci Rep
; 9(1): 12949, 2019 09 10.
Article
em En
| MEDLINE
| ID: mdl-31506471
ABSTRACT
Experimental techniques for identification of essential genes (EGs) in prokaryotes are usually expensive, time-consuming and sometimes unrealistic. Emerging in silico methods provide alternative methods for EG prediction, but often possess limitations including heavy computational requirements and lack of biological explanation. Here we propose a new computational algorithm for EG prediction in prokaryotes with an online database (ePath) for quick access to the EG prediction results of over 4,000 prokaryotes ( https//www.pubapps.vcu.edu/epath/ ). In ePath, gene essentiality is linked to biological functions annotated by KEGG Ortholog (KO). Two new scoring systems, namely, E_score and P_score, are proposed for each KO as the EG evaluation criteria. E_score represents appearance and essentiality of a given KO in existing experimental results of gene essentiality, while P_score denotes gene essentiality based on the principle that a gene is essential if it plays a role in genetic information processing, cell envelope maintenance or energy production. The new EG prediction algorithm shows prediction accuracy ranging from 75% to 91% based on validation from five new experimental studies on EG identification. Our overall goal with ePath is to provide a comprehensive and reliable reference for gene essentiality annotation, facilitating the study of those prokaryotes without experimentally derived gene essentiality information.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Células Procarióticas
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Algoritmos
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Bases de Dados Factuais
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Biologia Computacional
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Genes Essenciais
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Anotação de Sequência Molecular
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos