A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial co...

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Main Authors:	Jiang, Hongmei, An, Lingling, Lin, Simon M., Feng, Gang, Qiu, Yuqing
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3462201/

id	pubmed-3462201
recordtype	oai_dc
spelling	pubmed-34622012012-10-05 A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads Jiang, Hongmei An, Lingling Lin, Simon M. Feng, Gang Qiu, Yuqing Research Article The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm. Public Library of Science 2012-10-01 /pmc/articles/PMC3462201/ /pubmed/23049702 http://dx.doi.org/10.1371/journal.pone.0046450 Text en © 2012 Jiang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Jiang, Hongmei An, Lingling Lin, Simon M. Feng, Gang Qiu, Yuqing
spellingShingle	Jiang, Hongmei An, Lingling Lin, Simon M. Feng, Gang Qiu, Yuqing A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
author_facet	Jiang, Hongmei An, Lingling Lin, Simon M. Feng, Gang Qiu, Yuqing
author_sort	Jiang, Hongmei
title	A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
title_short	A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
title_full	A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
title_fullStr	A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
title_full_unstemmed	A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads
title_sort	statistical framework for accurate taxonomic assignment of metagenomic sequencing reads
description	The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3462201/
_version_	1611913150750261248

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

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