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Joint analysis of multiple metagenomic samples.

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 this stratification. Second, we propose a novel read clustering (also termed "binning") algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough.

SUBMITTER: Baran Y

PROVIDER: S-EPMC3280959 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Joint analysis of multiple metagenomic samples.

Baran Yael Y Halperin Eran E

PLoS computational biology 20120216 2

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 sampl ...[more]

PMID: 22359490

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Project description:Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Project description:BackgroundNumerous protocols for viral enrichment and genome amplification have been created. However, the direct identification of viral genomes from clinical specimens using next-generation sequencing (NGS) still has its challenges. As a selected viral nucleic acid extraction method may determine the sensitivity and reliability of NGS, it is still valuable to evaluate the extraction efficiency of different extraction kits using clinical specimens directly.ResultsIn this study, we performed qRT-PCR and viral metagenomic analysis of the extraction efficiency of four commonly used Qiagen extraction kits: QIAamp Viral RNA Mini Kit (VRMK), QIAamp MinElute Virus Spin Kit (MVSK), RNeasy Mini Kit (RMK), and RNeasy Plus Micro Kit (RPMK), using a mixed respiratory clinical sample without any pre-treatment. This sample contained an adenovirus (ADV), influenza virus A (Flu A), human parainfluenza virus 3 (PIV3), human coronavirus OC43 (OC43), and human metapneumovirus (HMPV). The quantity and quality of the viral extracts were significantly different among these kits. The highest threshold cycle(Ct)values for ADV and OC43 were obtained by using the RPMK. The MVSK had the lowest Ct values for ADV and PIV3. The RMK revealed the lowest detectability for HMPV and PIV3. The most effective rate of NGS data at 67.47% was observed with the RPMK. The other three kits ranged between 12.1-26.79% effectiveness rates for the NGS data. Most importantly, compared to the other three kits the highest proportion of non-host reads was obtained by the RPMK. The MVSK performed best with the lowest Ct value of 20.5 in the extraction of ADV, while the RMK revealed the best extraction efficiency by NGS analysis.ConclusionsThe evaluation of viral nucleic acid extraction efficiency is different between NGS and qRT-PCR analysis. The RPMK was most applicable for the metagenomic analysis of viral RNA and enabled more sensitive identification of the RNA virus genome in respiratory clinical samples. In addition, viral RNA extraction kits were also applicable for metagenomic analysis of the DNA virus. Our results highlighted the importance of nucleic acid extraction kit selection, which has a major impact on the yield and number of viral reads by NGS analysis. Therefore, the choice of extraction method for a given viral pathogen needs to be carefully considered.

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Joint analysis of multiple metagenomic samples.

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Joint analysis of multiple metagenomic samples.

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