Methods for high-throughput MethylCap-Seq data analysis

Abstract

BackgroundAdvances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. Currently there is a lack of workflows for efficient analysis of large, MethylCap-seq datasets containing multiple sample groups.MethodsThe NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. The workflow we describe performs MethylCap-seq experimental Quality Control (QC), sequence file processing and alignment, differential methylation analysis of multiple biological groups, hierarchical clustering, assessment of genome-wide methylation patterns, and preparation of files for data visualization.ResultsHere, we present a scalable, flexible workflow for MethylCap-seq QC, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. We demonstrate the experimental QC procedure with results from a large ovarian cancer study dataset and propose parameters which can identify problematic experiments. Promoter methylation profiling and hierarchical clustering analyses are demonstrated for four groups of acute myeloid leukemia (AML) patients. We propose a Global Methylation Indicator (GMI) function to assess genome-wide changes in methylation patterns between experimental groups. We also show how the workflow facilitates data visualization in a web browser with the application Anno-J.ConclusionsThis workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.

Aberrant DNA methylation of WNT pathway genes in the development and progression of CIMP-negative colorectal cancer

“…Sequencing results of all samples fulfilled the quality criteria, according to the MEDIPS quality control protocol. 34 Pearson correlation coefficient of saturation analysis exceeded the 0.5 threshold for every sample (0.71 § 0.07). Coverage of at least 5% of the methylation sites (11.43 § 2.56%) was more than 5 times for each sample during the CpG coverage analysis.…”

DNA Methylation Dynamics of Germinal Center B Cells Are Mediated by AID

“…Methyl-Cap-Seq is an affinity purification-based technique, which is likely not to be sufficiently sensitive to detect variable methylation changes in CpGs scattered throughout genome. Here we have used ERRBS, a genome-wide technique with higher coverage compared to Methyl-cap-Seq and single nucleotide level resolution (Rodriguez et al, 2012). We think that above differences are due to the experimental system and the techniques used in earlier studies.…”

Conserved effect of aging on DNA methylation and association with EZH2 polycomb protein in mice and humans

“…The generated Bam files were then analyzed using the MEDIPS R package and visualized on the Integrative Genomics Viewer (Chavez et al, 2010; Lienhard et al, 2014; Robinson et al., 2011; Rodriguez et al, 2012; Thorvaldsdottir et al, 2013). To assess if we have sufficient coverage for reliable and reproducible results, we first performed a saturation analysis (MEDIPS.saturation) and this showed sufficient sequencing depth.…”