title: Use of whole genome shotgun metagenomics: a practical guide for the microbiome-minded physician scientist.
abstract: Whole genome shotgun sequencing (WGS) has been increasingly recognized as the most comprehensive and robust approach for metagenomics research. When compared with 16S-based metagenomics, it offers the advantage of identification of species level taxonomy and the estimation of metabolic pathway activities from human and environmental samples. Several large-scale metagenomic projects have been recently conducted or are currently underway utilizing WGS. With the generation of vast amounts of data, the bioinformatics and computational analysis of WGS results become vital for the success of a metagenomics study. However, each step in the WGS data analysis, including metagenome assembly, gene prediction, taxonomy identification, function annotation, and pathway analysis, is complicated by the shear amount of data. Algorithms and tools have been developed specifically to handle WGS-generated metagenomics data with the hope of reducing the requirement on computational time and storage space. Here, we present an overview of the current state of metagenomics through WGS sequencing, challenges frequently encountered, and up-to-date solutions. Several applications that are uniquely applicable to microbiome studies in reproductive and perinatal medicine are also discussed.
authors.affiliation: Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine.
authors.forename: Jun
authors.initials: J
authors.lastname: Ma
authors.affiliation: Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine.
authors.forename: Amanda
authors.initials: A
authors.lastname: Prince
authors.affiliation: Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine.
authors.forename: Kjersti M
authors.initials: KM
authors.lastname: Aagaard
mesh_terms: D001419:Bacteria / Q000145:classification; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005260:Female; D015870:Gene Expression / Q000235:genetics / Q000502:physiology; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D054892:Metagenome / Q000235:genetics*; D056186:Metagenomics; D064307:Microbiota / Q000235:genetics* / Q000502:physiology*; D011247:Pregnancy; D018456:Reproductive Medicine