EcoliNet: A database of cofunctional gene network for Escherichia coli

Hanhae Kim, Jung Eun Shim, Junha Shin, Insuk Lee

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

During the past several decades, Escherichia coli has been a treasure chest for molecular biology. The molecular mechanisms of many fundamental cellular processes have been discovered through research on this bacterium. Although much basic research now focuses on more complex model organisms, E. coli still remains important in metabolic engineering and synthetic biology. Despite its long history as a subject of molecular investigation, more than one-third of the E. coli genome has no pathway annotation supported by either experimental evidence or manual curation. Recently, a network-assisted genetics approach to the efficient identification of novel gene functions has increased in popularity. To accelerate the speed of pathway annotation for the remaining uncharacterized part of the E. coli genome, we have constructed a database of cofunctional gene network with near-complete genome coverage of the organism, dubbed EcoliNet. We find that EcoliNet is highly predictive for diverse bacterial phenotypes, including antibiotic response, indicating that it will be useful in prioritizing novel candidate genes for a wide spectrum of bacterial phenotypes. We have implemented a web server where biologists can easily run network algorithms over EcoliNet to predict novel genes involved in a pathway or novel functions for a gene. All integrated cofunctional associations can be downloaded, enabling orthology-based reconstruction of gene networks for other bacterial species as well.

Original languageEnglish
Article numberbav001
JournalDatabase
Volume2015
DOIs
Publication statusPublished - 2015 Jan 1

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All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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