CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads

TL;DR

CompostBin is a novel DNA composition-based algorithm that accurately bins raw metagenomic reads into taxon-specific groups without requiring assembly or prior training, facilitating microbial diversity studies.

Contribution

It introduces a new method that bins raw environmental shotgun sequencing reads directly, bypassing the need for assembly or reference genomes, using PCA and normalized cut clustering.

Findings

Accurately bins simulated metagenomic data

Successfully classifies real metagenomic reads with known species

Operates without assembly or training on reference genomes

Abstract

A major hindrance to studies of microbial diversity has been that the vast majority of microbes cannot be cultured in the laboratory and thus are not amenable to traditional methods of characterization. Environmental shotgun sequencing (ESS) overcomes this hurdle by sequencing the DNA from the organisms present in a microbial community. The interpretation of this metagenomic data can be greatly facilitated by associating every sequence read with its source organism. We report the development of CompostBin, a DNA composition-based algorithm for analyzing metagenomic sequence reads and distributing them into taxon-specific bins. Unlike previous methods that seek to bin assembled contigs and often require training on known reference genomes, CompostBin has the ability to accurately bin raw sequence reads without need for assembly or training. It applies principal component analysis to project the data into an informative lower-dimensional space, and then uses the normalized cut clustering algorithm on this filtered data set to classify sequences into taxon-specific bins. We demonstrate the algorithm's accuracy on a variety of simulated data sets and on one metagenomic data set with known species assignments. CompostBin is a work in progress, with several refinements of the algorithm planned for the future.