Advanced computational algorithms for microbial community analysis using massive 16S rRNA sequence data

Abstract

With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence-based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.

Diversity, stability and resilience of the human gut microbiota

“…For example, obese persons harbor fewer types of microbes in their guts than lean persons, and lean and obese people differ significantly in abundances of specific taxa and functional genes 6,7,45 . Although community-level effects exists, and people can be classified as lean or obese with 90% accuracy based solely on their gut microbiota 46,47 , lean and obese individuals do not separate into distinct microbiota-based clusters on commonly used principal coordinates (PCoA) plots used to identify statistical differences between groups. Thus multiple statistical techniques are needed to fully reveal differences in microbiota correlated with different physiological states (Fig.…”

A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

“…16S rRNA sequences, the small unit of ribosomal RNA in prokaryotes, are the most widely used sequences for inferring the phylogenetic relations among microbial species [10]–[13]. The 16S rRNA based phylogenetic inference has revolutionized our view of microbial diversity and composition of many environments [14]–[17]. Many large-scale metagenomics projects have been undertaken to investigate various aspects of the microbial composition, e.g.…”

“…They found that pairwise alignments yielded a smaller distance than those from multiple alignments so that the use of pairwise alignment could reduce the inferred OTU number. Later, they illustrated the behavior of hierarchical and heuristic clustering algorithms in OTU construction, and concluded that hierarchical clustering algorithms are more accurate [17]. Huse et al [24] evaluated three different hierarchical clustering algorithms, and showed that the choice of clustering strategy could significantly affect the number of estimated OTUs.…”

Multivariate Approach for Studying Interactions between Environmental Variables and Microbial Communities

“…The rapid development of deep sequencing technologies has enabled us to measure in detail the composition of even most complex microbial communities in various environments, which has lead to a new world of knowledge [10], [11]. An increasing number of articles are being published on the human microbiome, showing correlations between microbiota profiles and different environmental or health characteristics.…”