A New Approach for Scalable Analysis of Microbial Communities

TL;DR

This paper introduces a scalable, reference-free n-gram based method for analyzing microbial communities from 16S rRNA data, significantly reducing data processing size and enabling advanced classification tasks.

Contribution

The authors present a novel n-gram sequence analysis technique that eliminates the need for taxonomic alignment, offering a more scalable approach for microbial community analysis.

Findings

Reduced data processing size by 105-fold

Effective classification across body sites and health states

Proposed continuous vector representations for deep learning

Abstract

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 tech- nique is based on n-gram sequence analysis of 16S rRNA data and reduces the processing data size dramatically (by 105 fold), without requiring taxonomic alignment. The proposed approach is applied to characterize phenotypic microbial community differ- ences in different settings. Specifically, we applied this approach in classification of microbial com- munities across different body sites, characterization of oral microbiomes associated with healthy and diseased individuals, and classification of microbial communities longitudinally during the develop- ment of infants. Different dimensionality reduction methods are introduced that offer a more scalable analysis framework, while minimizing the loss in classification accuracies. Among dimensionality re- duction techniques, we propose a continuous vector representation for microbial communities, which can widely be used for deep learning applications in microbial informatics.