Computational Omics Protocol for the Comparative Study of Microbiome Analysis

Exploring the microbiome involves an integrative approach of metagenomics, metabolomics, metatranscriptomics, and meta-proteomics combined with clinical or environmental metadata where metataxonomic analysis relies on sequencing and analysis of 16S rRNA genes. In this book chapter, we will summarize the present computational omics-based analytical strategies and tools such as R-bioconductor-based packages Phyloseq and Vegan, Linux-based qiime2, Birdman, ANCOM-BC, and Qiita as an open source for differential abundance analysis of various microbial taxa across clinical phenotypes or diseases which could provide us information regarding the association of microbial community with diseases. Moreover, it also gives insights into the integration of different strategies which could show significant advantages in fully describing microbiomes from multiple aspects. This book chapter shall also provide a review of various machine learning and statistical tests such as the Wilcox test, paired t-test, ANOVA, and linear regression tests for evaluating various significant correlation values between gut microbiome with host organs and diseases, confirming the importance of big-data mining in clinical practices and aiding researchers to get a pilot study on epigenetic mechanisms across gut microbiome and cancer.