Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Author:

Hempel Christopher A12ORCID,Wright Natalie1,Harvie Julia1,Hleap Jose S3,Adamowicz Sarah J1,Steinke Dirk12ORCID

Affiliation:

1. Department of Integrative Biology, University of Guelph , Guelph , ON N1G 2W1 , Canada

2. Centre for Biodiversity Genomics, University of Guelph , Guelph , ON N1G 2W1 , Canada

3. SHARCNET, University of Guelph , Guelph , ON N1G 2W1 , Canada

Abstract

Abstract Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Funder

Canada First Research Excellence Fund

University of Guelph

Publisher

Oxford University Press (OUP)

Subject

Genetics

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