Bioinformatics
CompostBin: a DNA composition-based algorithm for binning environmental shotgun reads
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
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Limited by the laboratory technique, traditional microorganism research usually focuses on one single individual species. This significantly limits the deep analysis of intricate biological processes among complex microorganism communities. With the rapid development of genome sequencing techniques, the traditional research methods of microorganisms based on the isolation and cultivation are gradually replaced by metagenomics, also known as environmental genomics. The first step, which is also the major bottleneck of metagenomic data analysis, is the identification and taxonomic characterization of the DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as "binning". Existing binning methods based on sequence similarity and sequence composition markers rely heavily on the reference genomes of known microorganisms and phylogenetic markers. Due to the limited availability of reference genomes and the bias and unstableness of markers, these methods may not be applicable in all cases. Not much unsupervised binning methods are reported, but the unsupervised nature of these methods makes them extremely difficult to annotate the clusters with taxonomic labels. In this paper, we present MetaCluster 2.0, an unsupervised binning method which could bin metagenomic sequencing datasets with high accuracy, and also identify unknown genomes and annotate them with proper taxonomic labels. The running time of MetaCluster 2.0 is at least 30 times faster than existing binning algorithms. MetaCluster 2.0, and all the test datasets mentioned in this paper are available at http://i.cs.hku.hk/~alse/MetaCluster/.