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Computational and Statistical Considerations in the Analysis of Metagenomic Data

Chapter in book
Authors Fredrik Boulund
Mariana Buongermino Pereira
Viktor Jonsson
Erik Kristiansson
Published in Metagenomics: Perspectives, Methods, and Applications
Pages 81-102
Publication year 2017
Published at Department of Mathematical Sciences
Pages 81-102
Language en
Keywords Differentially abundant genes, Gene quantification, High-dimensional data, High-performance computing, Normalization, Sequence mapping, Shotgun metagenomics
Subject categories Computer and Information Science

Abstract

In shotgun metagenomics, microbial communities are studied by random DNA fragments sequenced directly from environmental and clinical samples. The resulting data is massive, potentially consisting of billions of sequence reads describing millions of microbial genes. The data interpretation is therefore nontrivial and dependent on dedicated computational and statistical methods. In this chapter we discuss the many challenges associated with the analysis of shotgun metagenomic data. First, we address computational issues related to the quantification of genes in metagenomes. We describe algorithms for efficient sequence comparisons, recommended practices for setting up data workflows and modern high-performance computer resources that can be used to perform the analysis. Next, we outline the statistical aspects, including removal of systematic errors and how to identify differences between microbial communities from different experimental conditions. We conclude by underlining the increasing importance of efficient and reliable computational and statistical solutions in the analysis of large metagenomic datasets.

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