Since advances in next-generation sequencing (NGS) technique enabled to investigate uncultured microbiota and their genomes in unbiased manner, many microbiome researches have been reporting strong evidences for close links of microbiome to human health and disease. Bioinformatic and statistical analysis of NGS-based microbiome data are essential components in those microbiome researches to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. This chapter introduces bioinformatic analysis methods that generate taxonomy and functional feature count table along with phylogenetic tree from raw NGS microbiome data and then introduce statistical methods and machine learning approaches for analyzing the outputs of the bioinformatic analysis to infer the biodiversity of a microbial community and unravel host-microbiome association. Understanding the advantages and limitations of the analysis methods will help readers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research.

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Statistical Genomics

Brooke Fridley, Xuefeng Wang

SPRINGER-VERLAG NEW YORK

Humana Press

Methods in Molecular Biology, 1st ed. 2023, New York, NY, 2023

Methods in molecular biology, 2629, S.l, 2023

United States, United States of America

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Chapter 10: Bioinformatic and Statistical Analysis of Microbiome Data
1 Introduction
2 Datasets Used to Illustrate the Methods
3 Bioinformatic and Statistical Methods for Microbiome Data Analysis
3.1 Overview of Bioinformatic Pipeline for Raw Sequencing Data Analysis
3.2 Bioinformatic Analysis of Marker-Gene Sequencing Data
3.2.1 Sequencing Error Control and Variant Call
3.2.2 Taxonomic Classification
3.2.3 Phylogenetic Tree Construction
3.3 Bioinformatic Analysis of Metagenome Shotgun Sequencing Data
3.3.1 Quality Control and Decontamination
3.3.2 Reference-Based Taxonomy Identification
3.3.3 Reference-Based Functional Classification
3.3.4 De Novo Metagenomic Assembly Analysis
3.4 Statistical Analysis of Microbiome Data
3.4.1 Structure of Microbiome Data
3.4.2 Property of Microbiome Data
3.4.3 Quality Control of Microbiome Data
3.4.4 Normalization of Microbiome Data
3.4.5 Exploratory Analysis of Microbiome Data
3.4.6 Alpha Diversity
3.4.7 Beta Diversity
3.4.8 Microbiome-Wide Association Analysis
3.4.9 Community-Level Association Analysis Based on Alpha Diversity
3.4.10 Community-Level Association Analysis Based on Beta Diversity
3.4.11 Biodiversity-Free Test of Microbiome Community Association
3.4.12 Univariate Feature-Wise Associated Analysis Methods
3.4.13 Visualization of Univariate Association Analysis
3.4.14 Machine Learning Methods for Microbial Biomarker Discovery
4 Conclusions
References

This volume provides a collection of protocols from researchers in the statistical genomics field. Chapters focus on integrating genomics with other "omics" data, such as transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Statistical Genomics hopes that by covering these diverse and timely topics researchers are provided insights into future directions and priorities of pan-omics and the precision medicine era

2023-07-07