Optimizing taxonomic classification of marker gene amplicon sequences
- Published
- Accepted
- Subject Areas
- Bioinformatics, Microbiology, Taxonomy
- Keywords
- microbiome, marker-gene sequencing, taxonomy, sequence classification
- Copyright
- © 2018 Bokulich et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Optimizing taxonomic classification of marker gene amplicon sequences. PeerJ Preprints 6:e3208v2 https://doi.org/10.7287/peerj.preprints.3208v2
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 taxonomy classifiers that meet or exceed the accuracy of existing methods for marker-gene amplicon sequence classification. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods of VSEARCH, BLAST+, and SortMeRNA) for classification of marker-gene amplicon sequence data.
Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.
Author Comment
Version two contains additional data, including simulations on other 16S rRNA gene regions.