Masking as an effective quality control method for next-generation sequencing data analysis.

ABSTRACT:

Background

Next generation sequencing produces base calls with low quality scores that can affect the accuracy of identifying simple nucleotide variation calls, including single nucleotide polymorphisms and small insertions and deletions. Here we compare the effectiveness of two data preprocessing methods, masking and trimming, and the accuracy of simple nucleotide variation calls on whole-genome sequence data from Caenorhabditis elegans. Masking substitutes low quality base calls with 'N's (undetermined bases), whereas trimming removes low quality bases that results in a shorter read lengths.

Results

We demonstrate that masking is more effective than trimming in reducing the false-positive rate in single nucleotide polymorphism (SNP) calling. However, both of the preprocess

SUBMITTER: Yun S 

PROVIDER: S-EPMC4268903 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature