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.
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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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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: Kaufmann, M., Klinger, C. (eds) Functional Genomics. Methods in Molecular Biology, vol 815. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-424-7_7
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DOI: https://doi.org/10.1007/978-1-61779-424-7_7
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