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: ChIP-Seq is the standard method to identify genome-wide DNA-binding sites for transcription factors (TFs) and histone modifications. There is a growing need to analyze experiments with biological replicates, especially for epigenomic experiments where variation among biological samples can be substantial. However, tools that can perform group comparisons are currently lacking. RESULTS: We present a peak-calling prioritization pipeline (PePr) for identifying consistent or differential binding sites in ChIP-Seq experiments with biological replicates. PePr models read counts across the genome among biological samples with a negative binomial distribution and uses a local variance estimation method, ranking consistent or differential binding sites more favorably than sites with greater variability. We compared PePr with commonly used and recently proposed approaches on eight TF datasets and show that PePr uniquely identifies consistent regions with enriched read counts, high motif occurrence rate and known characteristics of TF binding based on visual inspection. For histone modification data with broadly enriched regions, PePr identified differential regions that are consistent within groups and outperformed other methods in scaling False Discovery Rate (FDR) analysis. AVAILABILITY AND IMPLEMENTATION: http://code.google.com/p/pepr-chip-seq/.