Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition.

ABSTRACT: An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvemen

SUBMITTER: Saeed I 

PROVIDER: S-EPMC3300000 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature