Abstract
Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other "omics" disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials.
Highlights
The diversity of species on earth is high, and most of them are microorganisms
The authors ascribed the poor performance of shotgun sequencing mainly to the weakness of the database used in the study, as compared to databases for the 16S ribosomal RNA (rRNA) gene
The use of multiple pipelines has been demonstrated to analyze and interpret the data starting from very raw sequence to the final statistical outputs
Summary
The diversity of species on earth is high, and most of them are microorganisms. Their ubiquitous presence makes it extremely difficult to identify and classify all microbes in a laboratory environment. 607 techniques, which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and researchers often try to correlate these microorganisms and their change with multiple treatment conditions (e.g., see [9]) Gene annotations in these studies support the association of specific genes or metabolic pathways with health and with specific diseases. Sometimes researchers may compare different environments for example to study antibiotic resistance genes (ARG) and understand which environments are more prone to such ARGs. no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. We provide an overview of different data analyses and statistical approaches to analyze metagenomics samples from a number of clinically derived datasets. Good metadata are key to good analyses and noise reduction in data analysis processes
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