ABSTRACT
The advent of next generation sequencing (NGS) technologies has revolutionised the study of microbial communities, enabling direct analysis of complex and diverse ecosystems in unprecedented detail. Metagenomics is the genetic analysis of microbial genomes recovered directly from environmental samples without the need for culturing the microorganisms. It has become a powerful tool for characterising the composition and function of microbial communities in diverse habitats (microbiota) such as soil, water, plants, fermented food, and the human gut. However, the vast amount of data generated by NGS technologies and the particularly flexible nature of the metagenomics approach in terms of choices that one has spanning sample type, metagenome extraction principles, targeted versus shotgun sequencing approach, and complexity of the data present significant challenges for data analysis, interpretation, and reporting of the results. Therefore, metagenomic analysis pipelines have been developed to facilitate the processing and interpretation of metagenomic data. Metagenomic data analysis involves several steps, including quality control of the sequence reads, extraction of taxonomic features, taxonomic classification, functional annotation, statistical analysis, and data visualisation. Different software tools have been developed to perform these tasks, each with its own unique features and limitations. This chapter focuses on two popular applications for microbial community analysis, viz., quantitative insights into microbial ecology (QIIME) and mothur, together offering a comprehensive suite of tools for analysing metagenomic data. The chapter begins with an introduction to microbiomes and metagenomics. It then delves into the software, detailing the tools' main features, strengths, and limitations. The chapter also provides step-by-step guidelines on how to use the software for quality control, taxonomic classification, phylogeny annotation, microbial community statistical analysis, and data visualisation. In addition to the standard operating protocols, the chapter discusses the challenges involved in analysing big sequence data and provides best practices to optimise the performance of metagenomic analysis pipelines, including guidelines for sample collection, optimising metagenome extraction and library preparation, the importance of mock community controls, selecting appropriate sequencing platforms and protocols, controlling for contamination and bias, and validating results. In conclusion, the standard operating procedures (SOPs) and best practices described in this chapter are relevant for researchers and analysts working with metagenomic data and will promote research consistency and contribute to the standardisation and reproducibility of metagenomic studies. Although significant progress has been made in NGS data analysis, challenges remain. Advances in artificial intelligence (AI) and machine learning (ML) are likely to play a critical role in overcoming these challenges and further advancing our understanding of microbial communities.
