Methodology article

Unsupervised statistical clustering of environmental shotgun sequences

Andrey Kislyuk¹ , Srijak Bhatnagar^1,2 , Jonathan Dushoff³ and Joshua S Weitz^1,4

¹School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA

²UC Davis Genome Center, University of California, Davis, Davis, CA 95616, USA

³Department of Biology, McMaster University, Hamilton, Ontario L8S 4K1, Canada

⁴School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA

author email corresponding author email

BMC Bioinformatics 2009, 10:316doi:10.1186/1471-2105-10-316

Published:	2 October 2009

Abstract

Background

The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed.

Results

We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k-mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin.

Conclusion

An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins.