Title Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2 ' s q 2-feature-classifier plugin Permalink

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. Background High-throughput sequencing technologies have transformed our ability to explore complex microbial communities, offering insight into microbial impacts on human health [1] and global ecosystems [2]. This is achieved most commonly by sequencing short, conserved marker genes amplified with ‘universal’ PCR primers, such as 16S rRNA genes for bacteria and archaea, or internal transcribed spacer (ITS) regions for fungi. Targeted marker-gene primers can also be used to profile specific taxa or functional groups, such as nifH genes [3]. These sequences often are compared against an annotated reference sequence database to determine the likely taxonomic origin of each sequence with as much specificity as possible. Accurate and specific taxonomic information is a crucial component of many experimental designs. Challenges in this process include the short length of typical sequencing reads with current technology, sequencing and PCR errors [4], selection of appropriate marker genes that contain sufficient heterogeneity to differentiate target species but that are homogeneous enough in some regions to design broad-spectrum primers, quality of reference sequence annotations [5], and selection of a method that accurately predicts the taxonomic affiliation of millions of sequences at low computational cost. Numerous methods have been developed for taxonomy classification of DNA sequences, but few have * Correspondence: nicholas.bokulich@nau.edu; benjamin.kaehler@anu.edu. au; gavin.huttley@anu.edu. au; gregcaporaso@gmail.com Equal contributors The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ 86011-4073, USA Research School of Biology, Australian National University, 46 Sullivans Creek Road, Acton ACT 2601, Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Bokulich et al. Microbiome (2018) 6:90 https://doi.org/10.1186/s40168-018-0470-z