Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
Genome biology, v 18(1), pp 182-182
21 Sep 2017
PMID: 28934964
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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.
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.
Metrics
Details
- Title
- Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
- Creators
- Alexa B R McIntyre - Cornell UniversityRachid Ounit - University of California, RiversideEbrahim Afshinnekoo - Cornell UniversityRobert J Prill - Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, USAElizabeth Hénaff - Cornell UniversityNoah Alexander - Cornell UniversitySamuel S Minot - One Codex, Reference Genomics, San Francisco, USADavid Danko - Cornell UniversityJonathan Foox - Cornell UniversitySofia Ahsanuddin - Cornell UniversityScott Tighe - University of VermontNur A Hasan - University of Maryland, College ParkPoorani Subramanian - Cosmos CorporationKelly Moffat - Cosmos CorporationShawn Levy - HudsonAlpha Institute for BiotechnologyStefano Lonardi - University of California, RiversideNick Greenfield - One Codex, Reference Genomics, San Francisco, USARita R Colwell - BloombergGail L Rosen - Drexel UniversityChristopher E Mason - Cornell University
- Publication Details
- Genome biology, v 18(1), pp 182-182
- Publisher
- Springer BMC
- Grant note
- R01 ES021006 / NIEHS NIH HHS R01 AI125416 / NIAID NIH HHS R25 EB020393 / NIBIB NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000411360100002
- Scopus ID
- 2-s2.0-85029756134
- Other Identifier
- 991019168563704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology
- Genetics & Heredity