Joint analysis of multiple metagenomic samples.

PLoS Computational Biology
Yael Baran, Eran Halperin

Abstract

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for ...Continue Reading

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Citations

May 23, 2014·Bioinformatics·Sohan SethAntti Honkela
Apr 30, 2013·Briefings in Functional Genomics·Bas E DutilhSacha A F T van Hijum
Oct 23, 2013·PLoS Computational Biology·Rogan CarrElhanan Borenstein
Sep 12, 2012·Briefings in Bioinformatics·Hanno Teeling, Frank Oliver Glöckner
Apr 1, 2017·BMC Bioinformatics·Jarno AlankoVeli Mäkinen
Oct 15, 2013·Genomics & Informatics·Mincheol KimHana Yi
Feb 11, 2015·Genome Research·Itai SharonJillian F Banfield

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Datasets Mentioned

BETA
ERA000116

Methods Mentioned

BETA
PCA
chemical treatment

Software Mentioned

MetaHIT
PLSA
AbundanceBin
BLAT
MetaSim
MultiBin
CompostBin

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