Xueqiu Lin1, Deqiang Sun, Benjamin Rodriguez, Qian Zhao, Hanfei Sun, Yong Zhang, Wei Li. 1. Department of Bioinformatics, School of Life sciences and Technology, Tongji University, Shanghai 20092, China, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
Abstract
MOTIVATION: Bisulfite sequencing (BS-seq) has emerged as the gold standard to study genome-wide DNA methylation at single-nucleotide resolution. Quality control (QC) is a critical step in the analysis pipeline to ensure that BS-seq data are of high quality and suitable for subsequent analysis. Although several QC tools are available for next-generation sequencing data, most of them were not designed to handle QC issues specific to BS-seq protocols. Therefore, there is a strong need for a dedicated QC tool to evaluate and remove potential technical biases in BS-seq experiments. RESULTS: We developed a package named BSeQC to comprehensively evaluate the quality of BS-seq experiments and automatically trim nucleotides with potential technical biases that may result in inaccurate methylation estimation. BSeQC takes standard SAM/BAM files as input and generates bias-free SAM/BAM files for downstream analysis. Evaluation based on real BS-seq data indicates that the use of the bias-free SAM/BAM file substantially improves the quantification of methylation level. AVAILABILITY AND IMPLEMENTATION: BSeQC is freely available at: http://code.google.com/p/bseqc/.
MOTIVATION:Bisulfite sequencing (BS-seq) has emerged as the gold standard to study genome-wide DNA methylation at single-nucleotide resolution. Quality control (QC) is a critical step in the analysis pipeline to ensure that BS-seq data are of high quality and suitable for subsequent analysis. Although several QC tools are available for next-generation sequencing data, most of them were not designed to handle QC issues specific to BS-seq protocols. Therefore, there is a strong need for a dedicated QC tool to evaluate and remove potential technical biases in BS-seq experiments. RESULTS: We developed a package named BSeQC to comprehensively evaluate the quality of BS-seq experiments and automatically trim nucleotides with potential technical biases that may result in inaccurate methylation estimation. BSeQC takes standard SAM/BAM files as input and generates bias-free SAM/BAM files for downstream analysis. Evaluation based on real BS-seq data indicates that the use of the bias-free SAM/BAM file substantially improves the quantification of methylation level. AVAILABILITY AND IMPLEMENTATION: BSeQC is freely available at: http://code.google.com/p/bseqc/.
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