Comparative analysis of primate DNA methylation on genome-wide scale
Speaker: Meromit Singer, cs@berkeley
Date: Monday, February 11 2013
Time: 9:00AM to 10:30AM
Location: Stata - Sem Rm G449 (Patil/ Kiva) *
Host: Manolis Kellis, MIT CSAIL
Contact: derek aylward, 6177154882, firstname.lastname@example.org
DNA methylation is a dynamic chemical modification that is abundant on DNA sequences and plays a central role in the regulatory mechanisms of cells. This modification can be inherited across cell divisions and generations, providing a ``memory-mechanism" for regulatory programs that is more flexible than that coded in the DNA sequence. In recent years, high-throughput sequencing technologies have enabled genome-wide annotation of DNA methylation. This, coupled with novel computational machinery, has enabled unperceivable insight to the characteristics, biological function and disease association of this phenomenon.
In this talk we will discuss several contributions to the field of high-throughput DNA methylation. I will first present a comparative study of genome-wide DNA methylation in three primate species: human, chimpanzee and orangutan, revealing that these species can be distinguished based on differences in DNA methylation that are independent of the underlying DNA sequence. This result is based on a novel algorithm that infers corrected site-specific methylation states given data from a cost-effective, but biased, experimental method. The ability to annotate DNA methylation at genome-wide scale leads to questions about the nature of methylation signatures in DNA, and an interesting computational question about how to recognize such signatures. We will discuss the question and a proposed solution that is optimal given biologically motivated assumptions. In the last part of the talk we will describe a systematic sequencing error we discovered during the analysis of a specialized methylation dataset. We will discuss why this error introduces false-positives to a broad range of high-throughput sequencing studies and will present a classifier to correct for such errors, showing that it performs very well with respect to both sensitivity and specificity.
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