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Microbial communities play important roles in the function and maintenance ofvarious biosystems, ranging from human body to the environment. Current methodsfor analysis of microbial communities are typically based on taxonomicphylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencingdata. In typical characterizations of microbial communities, studies deal withbillions of micobial sequences, aligning them to a phylogenetic tree. Weintroduce a new approach for the efficient analysis of microbial communities.Our new reference-free analysis tech- nique is based on n-gram sequenceanalysis of 16S rRNA data and reduces the processing data size dramatically (by105 fold), without requiring taxonomic alignment. The proposed approach isapplied to characterize phenotypic microbial community differ- ences indifferent settings. Specifically, we applied this approach in classification ofmicrobial com- munities across different body sites, characterization of oralmicrobiomes associated with healthy and diseased individuals, andclassification of microbial communities longitudinally during the develop- mentof infants. Different dimensionality reduction methods are introduced thatoffer a more scalable analysis framework, while minimizing the loss inclassification accuracies. Among dimensionality re- duction techniques, wepropose a continuous vector representation for microbial communities, which canwidely be used for deep learning applications in microbial informatics.

A New Approach for Scalable Analysis of Microbial Communities