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Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays

  • Qiang Jing
  • Andrew McLellan
  • John M. GreallyEmail author
  • Masako Suzuki
Part of the Methods in Molecular Biology book series (MIMB, volume 815)

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.

Key words

DNA methylation Computational analysis Bioinformatics Pipeline 

Notes

Acknowledgments

We wish to thank Shahina Maqbool, Raul Olea, and Gael Westby of Einstein’s Epigenomics Shared Facility for their contributions, and Einstein’s Center for Epigenomics.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Qiang Jing
    • 1
  • Andrew McLellan
    • 1
  • John M. Greally
    • 2
    Email author
  • Masako Suzuki
    • 1
  1. 1.Departments of Genetics (Computational Genetics) and Center for EpigenomicsAlbert Einstein College of MedicineBronxUSA
  2. 2.Albert Einstein College of MedicineBronxUSA

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