Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Authors

Aubrey R. Odom, Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Tyler Faits, Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Eduardo Castro-Nallar, Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca, Campus Talca, Avda. Lircay S/N, Talca, Chile.
Keith A. Crandall, Department of Biostatistics & Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
W Evan Johnson, Division of Infectious Disease, Center for Data Science, Rutgers University - New Jersey Medical School, Newark, NJ, USA. w.evan.johnson@rutgers.edu.

Document Type

Journal Article

Publication Date

8-26-2023

Journal

Scientific reports

Volume

13

Issue

1

DOI

10.1038/s41598-023-40799-x

Abstract

Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.

APA Citation

Odom, Aubrey R.; Faits, Tyler; Castro-Nallar, Eduardo; Crandall, Keith A.; and Johnson, W Evan, "Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data" (2023). GW Authored Works. Paper 3205.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/3205

Department

Biostatistics and Bioinformatics