MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data.

ABSTRACT: As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community's phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for pre

SUBMITTER: Wang X 

PROVIDER: S-EPMC4512773 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature