Yongseok Park1, Maria E Figueroa1, Laura S Rozek2, Maureen A Sartor2. 1. Department of Computational Medicine and Bioinformatics, Pathology Department, Department of Environmental Health Sciences, Department of Otolaryngology and Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. 2. Department of Computational Medicine and Bioinformatics, Pathology Department, Department of Environmental Health Sciences, Department of Otolaryngology and Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA Department of Computational Medicine and Bioinformatics, Pathology Department, Department of Environmental Health Sciences, Department of Otolaryngology and Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Abstract
MOTIVATION: DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. RESULTS: We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples. AVAILABILITY: methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. RESULTS: We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples. AVAILABILITY: methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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