Dataset Information

MethylSig: a whole genome DNA methylation analysis pipeline.

ABSTRACT: 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.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.methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software.Supplementary data are available at Bioinformatics online.

SUBMITTER: Park Y

PROVIDER: S-EPMC4147891 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

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MethylSig: a whole genome DNA methylation analysis pipeline.

Park Yongseok Y Figueroa Maria E ME Rozek Laura S LS Sartor Maureen A MA

Bioinformatics (Oxford, England) 20140516 17

<h4>Motivation</h4>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.<h4>R ...[more]

PMID: 24836530

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Project description:DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer .

Project description:Study objectivesWe hypothesized that DNA methylation patterns may contribute to disease severity or the development of hypertension and excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA).MethodsIllumina's (San Diego, CA, USA) DNA methylation 27-K assay was used to identify differentially methylated loci (DML). DNA methylation levels were validated by pyrosequencing. A discovery cohort of 15 patients with OSA and 6 healthy subjects, and a validation cohort of 72 patients with sleep disordered breathing (SDB).ResultsMicroarray analysis identified 636 DMLs in patients with OSA versus healthy subjects, and 327 DMLs in patients with OSA and hypertension versus those without hypertension. In the validation cohort, no significant difference in DNA methylation levels of six selected genes was found between the primary snoring subjects and OSA patients (primary outcome). However, a secondary outcome analysis showed that interleukin-1 receptor 2 (IL1R2) promoter methylation (-114 cytosine followed by guanine dinucleotide sequence [CpG] site) was decreased and IL1R2 protein levels were increased in the patients with SDB with an oxygen desaturation index > 30. Androgen receptor (AR) promoter methylation (-531 CpG site) and AR protein levels were both increased in the patients with SDB with an oxygen desaturation index > 30. Natriuretic peptide receptor 2 (NPR2) promoter methylation (-608/-618 CpG sites) were decreased, whereas levels of both NPR2 and serum C type natriuretic peptide protein were increased in the SDB patients with EDS. Speckled protein 140 (SP140) promoter methylation (-194 CpG site) was increased, and SP140 protein levels were decreased in the patients with SDB and EDS.ConclusionsIL1R2 hypomethylation and AR hypermethylation may constitute an important determinant of disease severity, whereas NPR2 hypomethylation and SP140 hypermethylation may provide a biomarker for vulnerability to EDS in OSA.CommentaryA commentary on this article appears in this issue on page 723.

Dataset Information

MethylSig: a whole genome DNA methylation analysis pipeline.

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MethylSig: a whole genome DNA methylation analysis pipeline.

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