Abstract
Rapid advancement in high-throughput sequencing technologies with a better computational framework (software and hardware) has completely altered microbial community analysis. Microbial communities can be understood by genomic information, mostly through gene amplification (like 16S/18S rRNA) or whole metagenome shotgun (WMGS) sequencing, providing insights into the diversity and different ecological, metabolic, and physiological functions of microorganisms. However, the unique characteristics of the big data produced by these technologies limit the process of drawing valid biological inferences. The statistical and computational challenges include data normalization and accurate quantification of microbial taxonomy, relative gene abundance, and their metabolic capabilities; precise phylogenetic placement of genomes; and multivariate but sparse analysis of high-dimensional compositional data. This chapter discusses the current strategies for analysis and available tools that can be applied to amplicon and shotgun metagenomic data and also lists some of the limitations associated with their use. It also discusses the best-practice protocols for sequence preprocessing, clustering, annotation, visualization, and future research directions.
Published Version
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