Automated computational analysis of genome-wide DNA methylation profiling data from HELP-tagging assays. Academic Article uri icon


  • 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.

published proceedings

  • Methods Mol Biol

altmetric score

  • 0.5

author list (cited authors)

  • Jing, Q., McLellan, A., Greally, J. M., & Suzuki, M.

citation count

  • 13

publication date

  • January 2012