title: Methods for high-throughput MethylCap-Seq data analysis.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.
authors.affiliation: The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
authors.forename: Benjamin A T
authors.initials: BA
authors.lastname: Rodriguez
authors.forename: David
authors.initials: D
authors.lastname: Frankhouser
authors.forename: Mark
authors.initials: M
authors.lastname: Murphy
authors.forename: Michael
authors.initials: M
authors.lastname: Trimarchi
authors.forename: Hok-Hei
authors.initials: HH
authors.lastname: Tam
authors.forename: John
authors.initials: J
authors.lastname: Curfman
authors.forename: Rita
authors.initials: R
authors.lastname: Huang
authors.forename: Michael W Y
authors.initials: MW
authors.lastname: Chan
authors.forename: Hung-Cheng
authors.initials: HC
authors.lastname: Lai
authors.forename: Deval
authors.initials: D
authors.lastname: Parikh
authors.forename: Bryan
authors.initials: B
authors.lastname: Ball
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schwind
authors.forename: William
authors.initials: W
authors.lastname: Blum
authors.forename: Guido
authors.initials: G
authors.lastname: Marcucci
authors.forename: Pearlly
authors.initials: P
authors.lastname: Yan
authors.forename: Ralf
authors.initials: R
authors.lastname: Bundschuh
mesh_terms: D016000:Cluster Analysis; D018899:CpG Islands; D004247:DNA / Q000032:analysis* / Q000592:standards; D019175:DNA Methylation; D005260:Female; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D011786:Quality Control; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000592:standards
chemical_list: D004247:DNA