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

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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.<br /><br />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 preprocessing methods did not affect the false-negative rate in SNP calling with statistical significance compared to the data analysis without preprocessing. False-positive rate and false-negative rate for small insertions and deletions did not show differences between masking and trimming.<br /><br />CONCLUSIONS: We recommend masking over trimming as a more effective preprocessing method for next generation sequencing data analysis since masking reduces the false-positive rate in SNP calling without sacrificing the false-negative rate although trimming is more commonly used currently in the field. The perl script for masking is available at http://code.google.com/p/subn/. The sequencing data used in the study were deposited in the Sequence Read Archive (SRX450968 and SRX451773).

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APA

Yun, S., & Yun, S. (2014). Masking as an effective quality control method for next-generation sequencing data analysis. BMC Bioinformatics, 15(1). https://doi.org/10.1186/s12859-014-0382-2

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