Functional gene networks based on the gene neighborhood in metagenomes

Chan Yeong Kim, Insuk Lee

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The gene neighborhood in prokaryotic genomes has been effectively utilized in inferring co-functional networks in various organisms. Previously, such genomic context information has been sought among completely assembled prokaryotic genomes. Here, we present a method to infer functional gene networks according to the gene neighborhood in metagenome contigs, which are incompletely assembled genomic fragments. Given that the amount of metagenome sequence data has now surpassed that of completely assembled prokaryotic genomes in the public domain, we expect benefits of inferring networks by the metagenome-based gene neighborhood. We generated co-functional networks for diverse taxonomical species using metagenomics contigs derived from the human microbiome and the ocean microbiome. We found that the networks based on the metagenome gene neighborhood outperformed those based on 1748 completely assembled prokaryotic genomes. We also demonstrated that the metagenome-based gene neighborhood could predict genes related to virulence-associated phenotypes in a bacterial pathogen, indicating that metagenome-based functional links could be sufficiently predictive for some phenotypes of medical importance. Owing to the exponential growth of metagenome sequence data in public repositories, metagenome-based inference of co-functional networks will facilitate understanding of gene functions and pathways in diverse species.

Original languageEnglish
Pages (from-to)301-306
Number of pages6
JournalAnimal Cells and Systems
Issue number5
Publication statusPublished - 2017 Sept 3

Bibliographical note

Funding Information:
This work was supported by a grant from the National Research Foundation of Korea [grant number 2015R1A2A1A15055859], [grant number 2017M3A9B4042581] to I.L.

Publisher Copyright:
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Animal Science and Zoology
  • Biochemistry, Genetics and Molecular Biology(all)


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