Methods for high-throughput MethylCap-Seq data analysis.

Benjamin A T Rodriguez, David Frankhouser, Mark Murphy, Michael Trimarchi, Hok Hei Tam, John Curfman, Rita Huang, Michael W Y Chan, Hung Cheng Lai, Deval Parikh, Bryan Ball, Sebastian Schwind, William Blum, Guido Marcucci, Pearlly Yan, Ralf Bundschuh

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Advances 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. The 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. Here, 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. This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.

Original languageEnglish
Article numberS14
JournalBMC Genomics
Volume13 Suppl 6
Publication statusPublished - 2012
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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  • Cite this

    Rodriguez, B. A. T., Frankhouser, D., Murphy, M., Trimarchi, M., Tam, H. H., Curfman, J., Huang, R., Chan, M. W. Y., Lai, H. C., Parikh, D., Ball, B., Schwind, S., Blum, W., Marcucci, G., Yan, P., & Bundschuh, R. (2012). Methods for high-throughput MethylCap-Seq data analysis. BMC Genomics, 13 Suppl 6, [S14].