Computational Techniques Used for Microbial Diversity Analysis

Abstract

To understand the structural and functional relationships between microorganisms within a complex microbial community and/or with their host, it is important to study them as one rather than studying them separately. Next-generation sequencing (NGS)-based approaches evolved in the last decades like 16S rRNA gene amplicon and metagenome sequencing revolutionize the microbiome field by providing huge amount of data about structure and functions of microbial communities from diverse ecosystems. In 16S rRNA gene amplicon sequencing, one or multiple variable regions from 16S rRNA gene (phylogenetic marker) are targeted and amplified using universal primers followed by sequencing. Due to its accuracy to capture broad range of taxa from microbial communities and low sequencing cost, 16S rRNA amplicon sequencing has become popular technique to study microbiome in the last decade. For the downstream analysis of amplicon sequencing data, OTU (operational taxonomic unit)- or ASV (amplicon sequence variant)-based approaches using tools like QIIME1, USEARCH, MOTHUR, DADA2, and QIIME2, respectively, are used. Calculation of alpha and beta diversities, multivariate analysis, marker prediction etc. can be done in both approaches after getting the abundance table of OTUs/ASVs. This chapter will give a brief description of each and every step involved in amplicon sequence data analysis by using OTU- or ASV-based approaches using QIIME1 and DADA2 along with some downstream analyses like ordination and marker prediction using ALDEx2. National Centre for Microbial Resource, National Centre for Cell Science