High-resolution characterization of the human microbiome

Abstract

The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically employ targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically- and clinically-relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.

Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome

“…Recently, a plethora of comparative studies have identified intriguing associations between the composition of the microbiome and numerous diseases including various metabolic disorders (Greenblum et al, 2012; Karlsson et al, 2014; Qin et al, 2013), malignancies (Schulz et al, 2014), autoimmune diseases (Scher et al, 2013), and neurological developmental disorders (Hsiao et al, 2013). Such studies can take two different approaches to profile the composition of the microbiome (Noecker et al, 2016). The first approach focuses on taxonomy, aiming to profile the abundances of different microbial clades in each sample, either by targeted sequencing of the ribosomal 16S gene and operational taxonomic units clustering (Caporaso et al, 2010; Schloss et al, 2009) or by shotgun metagenomic sequencing and quantification of clade-specific marker genes’ abundances (Segata et al, 2012).…”

Can Consideration of the Microbiome Improve Antimicrobial Utilization and Treatment Outcomes in the Oncology Patient?

“…In addition to 16S rRNA gene sequencing, microbiome characterization methodologies have expanded to other “-omics” approaches to include whole genome shotgun sequencing, RNAseq, and metabolomics, which more precisely delineate bacterial community structure, gene presence/expression, and metabolic activity [7]. Use of these methodologies has illuminated that the microflora have profound effects on human health such as altering cytokine profiles, influencing inflammatory immune responses, and altering metabolites [8–11].…”

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis

“…Researchers across many life and clinical science disciplines are increasingly exploring the impact of microbes and microbial community dynamics. Concomitant increases are occurring in microbiome data generation as well as in the development of computational tools to perform data analysis (Aguiar-Pulido et al, 2016;Morgan and Huttenhower, 2014;Noecker et al, 2017). However, many researchers studying the microbiome lack resources or specialized skills for managing and analyzing the large, multi-dimensional datasets associated with metagenomic analyses.…”