Large-scale machine learning for metagenomics sequence classification.

ABSTRACT:

Motivation

Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.

Results

We propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of met

SUBMITTER: Vervier K 

PROVIDER: S-EPMC4896366 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature