A New Approach for Scalable Analysis of Microbial Communities

Motivation: Microbial communities play important roles in the function and maintenance of various biosystems, ranging from human body to the environment. Current methods for analysis of microbial communities are typically based on taxonomic phylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencing data. In typical characterizations of microbial communities, studies deal with billions of micobial sequences, aligning them to a phylogenetic tree. We introduce a new approach for the efficient analysis of microbial communities. Our new reference-free analysis technique is based on n-gram sequence analysis of 16S rRNA data and reduces the processing data size dramatically (by 10 fold), without requiring taxonomic alignment. Results: The proposed approach is applied to characterize phenotypic microbial community differences in different settings. Specifically, we applied this approach in classification of microbial communities across different body sites, characterization of oral microbiomes associated with healthy and diseased individuals, and classification of microbial communities longitudinally during the development of infants. Different dimensionality reduction methods are introduced that offer a more scalable analysis framework, while minimizing the loss in classification accuracies. Among dimensionality reduction techniques, we propose a continuous vector representation for microbial communities, which can widely be used for deep learning applications in microbial informatics. Availability: The Matlab code and data will be available on: http://llp.berkeley.edu. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.