Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

Billy Taj, Mobolaji Adeolu, Xuejian Xiong, Jordan Ang, Nirvana Nursimulu, John Parkinson
doi: https://doi.org/10.1101/2021.02.23.432558
Billy Taj
1Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada M5G 0A4
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mobolaji Adeolu
1Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada M5G 0A4
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xuejian Xiong
1Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada M5G 0A4
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jordan Ang
2Department of Chemical and Physical Sciences, University of Toronto, Mississauga, Ontario, Canada L5L 1C6
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nirvana Nursimulu
1Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada M5G 0A4
3Department of Computer Science, University of Toronto, Toronto, ON, Canada M5S 3G4
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
John Parkinson
1Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada M5G 0A4
4Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 3G4
5Department of Biochemistry, University of Toronto, Toronto, ON, Canada M5S 3G4
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: john.parkinson@utoronto.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Background Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles.

Results Here we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single end or paired end reads) and processes them through a tiered series of filtering, assembly and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state of the art pipelines and demonstrate improved performance and functionality.

Conclusion MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes is made freely available at https://github.com/ParkinsonLab/MetaPro. The software is freely available under the GNU general public license v3.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted February 23, 2021.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
Billy Taj, Mobolaji Adeolu, Xuejian Xiong, Jordan Ang, Nirvana Nursimulu, John Parkinson
bioRxiv 2021.02.23.432558; doi: https://doi.org/10.1101/2021.02.23.432558
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
Billy Taj, Mobolaji Adeolu, Xuejian Xiong, Jordan Ang, Nirvana Nursimulu, John Parkinson
bioRxiv 2021.02.23.432558; doi: https://doi.org/10.1101/2021.02.23.432558

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3597)
  • Biochemistry (7563)
  • Bioengineering (5517)
  • Bioinformatics (20777)
  • Biophysics (10316)
  • Cancer Biology (7973)
  • Cell Biology (11629)
  • Clinical Trials (138)
  • Developmental Biology (6602)
  • Ecology (10197)
  • Epidemiology (2065)
  • Evolutionary Biology (13605)
  • Genetics (9537)
  • Genomics (12842)
  • Immunology (7919)
  • Microbiology (19536)
  • Molecular Biology (7653)
  • Neuroscience (42050)
  • Paleontology (307)
  • Pathology (1257)
  • Pharmacology and Toxicology (2199)
  • Physiology (3266)
  • Plant Biology (7036)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5426)
  • Zoology (1115)