MTGIpick allows robust identification of genomic islands from a single genome

Qi Dai, Chaohui Bao, Yabing Hai, Sheng Ma, Tao Zhou, Cong Wang, Yunfei Wang, Wenwen Huo, Xiaoqing Liu, Yuhua Yao, Zhenyu Xuan, Min Chen, Michael Q. Zhang

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showingmultiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizesmissed by existingmethods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.

Original languageEnglish (US)
Pages (from-to)361-373
Number of pages13
JournalBriefings in Bioinformatics
Volume19
Issue number3
DOIs
StatePublished - May 1 2018
Externally publishedYes

Keywords

  • Boundary detection
  • Feature selection
  • Genomic island detection
  • Genomic signature
  • Multiscale statistical test

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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  • Cite this

    Dai, Q., Bao, C., Hai, Y., Ma, S., Zhou, T., Wang, C., Wang, Y., Huo, W., Liu, X., Yao, Y., Xuan, Z., Chen, M., & Zhang, M. Q. (2018). MTGIpick allows robust identification of genomic islands from a single genome. Briefings in Bioinformatics, 19(3), 361-373. https://doi.org/10.1093/bib/bbw118