Chapter title |
Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays.
|
---|---|
Chapter number | 7 |
Book title |
Functional Genomics
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Published in |
Methods in molecular biology, November 2011
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DOI | 10.1007/978-1-61779-424-7_7 |
Pubmed ID | |
Book ISBNs |
978-1-61779-423-0, 978-1-61779-424-7
|
Authors |
Jing Q, McLellan A, Greally JM, Suzuki M, Qiang Jing, Andrew McLellan, John M. Greally, Masako Suzuki |
Editors |
Michael Kaufmann, Claudia Klinger |
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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Mendeley readers
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Student > Postgraduate | 2 | 12% |
Student > Ph. D. Student | 2 | 12% |
Professor > Associate Professor | 2 | 12% |
Lecturer | 1 | 6% |
Other | 1 | 6% |
Unknown | 5 | 29% |
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Environmental Science | 1 | 6% |
Other | 1 | 6% |
Unknown | 5 | 29% |