Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.

ABSTRACT: As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outliers. However, the training genomic data (bacterial and archaeal genomes) usually contain a portion of outliers, which come from sequencing errors, phage invasions, and some highly expressed genes, etc. The outliers, treated as noises, prohibit the development of classifiers with better prediction a

SUBMITTER: Hou T 

PROVIDER: S-EPMC4309676 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature