PSORTm: A bacterial and archaeal protein subcellular localization prediction tool for metagenomics data

Michael A. Peabody, Wing Yin Venus Lau, Gemma R. Hoad, Baofeng Jia, Finlay Maguire, Kristen L. Gray, Robert G. Beiko, Fiona S.L. Brinkman

Résultat de recherche: Articleexamen par les pairs

14 Citations (Scopus)

Résumé

Motivation: Many methods for microbial protein subcellular localization (SCL) prediction exist; however, none is readily available for analysis of metagenomic sequence data, despite growing interest from researchers studying microbial communities in humans, agri-food relevant organisms and in other environments (e.g. for identification of cell-surface biomarkers for rapid protein-based diagnostic tests). We wished to also identify new markers of water quality from freshwater samples collected from pristine versus pollution-impacted watersheds. Results: We report PSORTm, the first bioinformatics tool designed for prediction of diverse bacterial and archaeal protein SCL from metagenomics data. PSORTm incorporates components of PSORTb, one of the most precise and widely used protein SCL predictors, with an automated classification by cell envelope. An evaluation using 5-fold cross-validation with in silico-fragmented sequences with known localization showed that PSORTm maintains PSORTb's high precision, while sensitivity increases proportionately with metagenomic sequence fragment length. PSORTm's read-based analysis was similar to PSORTb-based analysis of metagenome-assembled genomes (MAGs); however, the latter requires non-trivial manual classification of each MAG by cell envelope, and cannot make use of unassembled sequences. Analysis of the watershed samples revealed the importance of normalization and identified potential biomarkers of water quality. This method should be useful for examining a wide range of microbial communities, including human microbiomes, and other microbiomes of medical, environmental or industrial importance.

Langue d'origineEnglish
Pages (de-à)3043-3048
Nombre de pages6
JournalBioinformatics
Volume36
Numéro de publication10
DOI
Statut de publicationPublished - mai 1 2020

Note bibliographique

Funding Information:
This work was primarily supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN grant to F.S.L.B., with additional financial support by Genome Canada/Genome BC and Simon Fraser University.

Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press. All rights reserved.

ASJC Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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