ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.

ABSTRACT:

Motivation

Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.

Results

There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of t

SUBMITTER: Koslicki D 

PROVIDER: S-EPMC4619776 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature