Unsupervised two-way clustering of metagenomic sequences

Journal of Biomedicine & Biotechnology
Shruthi Prabhakara, Raj Acharya

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.

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Citations

Feb 4, 2012·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Yi WangFrancis Y L Chin

Related Concepts

In Silico
Disease Clustering
Genome, Bacterial
Online Mendelian Inheritance In Man
Metagenomics
Genome
Biniou protein, Drosophila
Species
Metagenome

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