Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays
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 wordsDNA methylation Computational analysis Bioinformatics Pipeline
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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