Automated computational analysis of genome-wide DNA methylation profiling data from HELP-tagging assays

Qiang Jing, Andrew McLellan, John M. Greally, Masako Suzuki

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations

Abstract

A novel DNA methylation assay, HELP-tagging, has been recently described to use massively parallel sequencing technology for genome-wide methylation profiling. Massively parallel sequencing-based assays such as this produce substantial amounts of data, which complicate analysis and necessitate the use of significant computational resources. To simplify the processing and analysis of HELP-tagging data, a bioinformatic analytical pipeline was developed. Quality checks are performed on the data at various stages, as they are processed by the pipeline to ensure the accuracy of the results. A quantitative methylation score is provided for each locus, along with a confidence score based on the amount of information available for determining the quantification. HELP-tagging analysis results are supplied in standard file formats (BED and WIG) that can be readily examined on the UCSC genome browser.

Original languageEnglish (US)
Title of host publicationFunctional Genomics
Subtitle of host publicationMethods and Protocols
EditorsMichael Kaufmann, Claudia Klinger
Pages79-87
Number of pages9
DOIs
StatePublished - Jan 2 2012

Publication series

NameMethods in Molecular Biology
Volume815
ISSN (Print)1064-3745

Keywords

  • Bioinformatics
  • Computational analysis
  • DNA methylation
  • Pipeline

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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  • Cite this

    Jing, Q., McLellan, A., Greally, J. M., & Suzuki, M. (2012). Automated computational analysis of genome-wide DNA methylation profiling data from HELP-tagging assays. In M. Kaufmann, & C. Klinger (Eds.), Functional Genomics: Methods and Protocols (pp. 79-87). (Methods in Molecular Biology; Vol. 815). https://doi.org/10.1007/978-1-61779-424-7_7