Unsupervised Two-Way Clustering of Metagenomic Sequences

Shruthi Prabhakara,Raj Acharya

doi:10.1155/2012/153647

Abstract

A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.

Highlights

Metagenomics is defined as the study of genomic content of microbial communities in their natural environments, bypassing the need for isolation and laboratory cultivation of individual species [1]
The distribution of word counts along a genome can be approximated as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare [21]
As the “true solution” for sequence data generated from most metagenomic studies is still unknown, we focused on synthetic datasets for benchmarking

Summary

Introduction

Metagenomics is defined as the study of genomic content of microbial communities in their natural environments, bypassing the need for isolation and laboratory cultivation of individual species [1]. Its importance arises from the fact that over 99% of the species yet to be discovered are resistant to cultivation [2]. This limitation imposed by cultivation of isolated clones has severely skewed our view of microbial diversity. Many computational challenges arise while analyzing deep sequence data from heterogeneous populations [3]. The computational method we present here aims to quantify the microbial diversity within a metagenome based on a set of deep sequencing reads

Objectives

Methods

Conclusion