Optimizing Taxonomic Classification of Marker-gene Amplicon Sequences with QIIME 2's Q2-feature-classifier Plugin

Overview

Journal Microbiome

Publisher Biomed Central

Specialties Genetics
Microbiology

Date 2018 May 19

PMID 29773078

Citations 1924

Authors

Nicholas A Bokulich

Benjamin D Kaehler

Jai Ram Rideout

Matthew Dillon

Evan Bolyen

Rob Knight

Gavin A Huttley

J Gregory Caporaso

Affiliations

Soon will be listed here.

Abstract

Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis.

Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ).

Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

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