title: Automated computational analysis of genome-wide DNA methylation profiling data from HELP-tagging assays.
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.
authors: Departments of Genetics (Computational Genetics) and Center for Epigenomics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, USA.
authors: Qiang
authors: Q
authors: Jing
authors: Andrew
authors: A
authors: McLellan
authors: John M
authors: JM
authors: Greally
authors: Masako
authors: M
authors: Suzuki
mesh_terms: D000465:Algorithms; D057205:Automation, Laboratory; D001483:Base Sequence; D003198:Computer Simulation; D018899:CpG Islands; D019175:DNA Methylation; D015342:DNA Probes / Q000138:chemical synthesis; D044127:Epigenesis, Genetic; D015698:Genomic Library; D008957:Models, Genetic; D016415:Sequence Alignment / Q000379:methods; D012984:Software
chemical_list: D015342:DNA Probes