FlowClus: efficiently filtering and denoising pyrosequenced amplicons.

ABSTRACT:

Background

Reducing the effects of sequencing errors and PCR artifacts has emerged as an essential component in amplicon-based metagenomic studies. Denoising algorithms have been designed that can reduce error rates in mock community data, but they change the sequence data in a manner that can be inconsistent with the process of removing errors in studies of real communities. In addition, they are limited by the size of the dataset and the sequencing technology used.

Results

FlowClus uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. When used to analyze a mock community dataset, Fl

SUBMITTER: Gaspar JM 

PROVIDER: S-EPMC4380255 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature