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Genome Biol. 2017 Sep 21;18(1):182. doi: 10.1186/s13059-017-1299-7.

Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Author information

1
Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.
2
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.
3
The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA.
4
Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA.
5
School of Medicine, New York Medical College, Valhalla, NY, 10595, USA.
6
Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA, 95120, USA.
7
One Codex, Reference Genomics, San Francisco, CA, 94103, USA.
8
University of Vermont, Burlington, VT, 05405, USA.
9
CosmosID, Inc, Rockville, MD, 20850, USA.
10
Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies (UMIACS), College Park, MD, 20742, USA.
11
HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.
12
Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
13
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104, USA. gail.l.rosen@gmail.com.
14
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA. chm2042@med.cornell.edu.
15
The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA. chm2042@med.cornell.edu.
16
The Feil Family Brain and Mind Research Institute, New York, NY, 10065, USA. chm2042@med.cornell.edu.

Abstract

BACKGROUND:

One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited.

RESULTS:

In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages.

CONCLUSIONS:

This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

KEYWORDS:

Classification; Comparison; Ensemble methods; Meta-classification; Metagenomics; Pathogen detection; Shotgun sequencing; Taxonomy

PMID:
28934964
PMCID:
PMC5609029
DOI:
10.1186/s13059-017-1299-7
[Indexed for MEDLINE]
Free PMC Article

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