  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.
