IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNA gene amplicon sequencing data.

ABSTRACT:

Background

The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods.

Results

The newly proposed denoising algorithm IPED includes a machine learning method which predicts potentially erroneous positions in sequencing reads based on a combination of quality metrics. Subsequently, this information is used to group those error-containing reads with correct reads, resulting in error-free consensus

SUBMITTER: Mysara M 

PROVIDER: S-EPMC4850673 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature