Bioinformatics Analysis of Epigenomic Methylation Patterns in the Era of Massively Parallel Sequencing
Mark D. Robinson, Bryan Beresford-Smith, Anthony Kaspi and I. Haviv
from: Epigenetics: A Reference Manual (Edited by: Jeffrey M. Craig and Nicholas C. Wong). Caister Academic Press, U.K. (2011)
Since biological phenotype and differentiation is regulated partially through CpG DNA methylation, and this mark is relatively easy to measure, genome-wide profiling of methylation landscape is a popular tool in epigenetic research. The burden then falls on bioinformatics to provide normalization, quality control, and interpretation, while adopting to the vast number of versatile methods to interrogate methylation profiles. While creating a relational report of the results from either of these methods is critical for data to cross over from lab to lab, and from research to diagnostic and translation purposes, the methods are each introducing their unique bias to the data. Ideally, one would hope all labs would adopt a single method, but since each method offers a unique advantage, such as price, sensitivity, or confidence, one has to overcome the disparity hurdle via bioinformatic tools. Thus identifying the methodological biases of each technique, and the way to compensate for those in the process of generating a universal CpG methylation prediction on a genome region is the ultimate goal we are describing here. Follow up of CpG methylation with other read outs, such as impact on gene expression or coincidence with locations in the genome, where allelic variation is associated with the investigated phenotype, are also key to proper interpretation of the results read more ...