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 (for example, 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 vs 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. AVAILABILITY AND IMPLEMENTATION: Documentation, source code, and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open source software under GNU General Public License Version 3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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 (for example, 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 vs 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. AVAILABILITY AND IMPLEMENTATION: Documentation, source code, and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open source software under GNU General Public License Version 3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Wing Yin Venus Lau; Gemma R Hoad; Vivian Jin; Geoffrey L Winsor; Ashmeet Madyan; Kristen L Gray; Matthew R Laird; Raymond Lo; Fiona S L Brinkman Journal: Nucleic Acids Res Date: 2021-01-08 Impact factor: 16.971