Computational and Statistical Considerations in the Analysis of Metagenomic Data
Kapitel i bok, 2017

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.

Differentially abundant genes

Gene quantification

High-dimensional data

Shotgun metagenomics

Normalization

Sequence mapping

High-performance computing

Författare

Fredrik Boulund

Karolinska universitetssjukhuset

Mariana Buongermino Pereira

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Viktor Jonsson

Göteborgs universitet

Chalmers, Matematiska vetenskaper

Erik Kristiansson

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Metagenomics: Perspectives, Methods, and Applications

81-102

Ämneskategorier

Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1016/B978-0-08-102268-9.00004-5

Mer information

Senast uppdaterat

2019-03-15