STAGE ISSS: Shili Lin

Join us for the next instalment of the STAGE International Speaker Seminar Series (ISSS) with

Shili Lin

Professor of Statistics, College of Arts and Sciences
Professor of Biostatistics, College of Public
Health Visiting Professor, Mathematical Biosciences Institute
Ohio State University

Talk Title:

Addressing the Correlated Feature in Sequencing-Based DNA Methylation
Data for Detection of Differentially Methylated Regions

Abstract:

DNA methylation is an epigenetic change occurring in genomic CpG sequences that contribute to the regulation of gene transcription both in normal and malignant cells. In recent years, aided by fast parallel sequencing technology, a number of genome-wide platforms have been developed to provide high throughput DNA methylation data. They can be classifed as bisulfte-based (BS-seq) or capture-based (Cap-seq), both producing a massive amount of data. Numerous sophisticated statistical methods have been developed to analyze both types of data, but methods for BS-seq data are mainly for detecting diferentially methylated loci (DMLs), although diferentially methylated regions (DMRs) are often of more relevance biologically. The most prominent feature that is often overlooked in DML detection methods is the correlation in methylation signals in neighboring CpG sites. In this talk, I will discuss several statistical methods for analyzing both BS-seq and Cap-seq data for detecting DMRs between two groups. In particular, I will highlight methods that take correlations into consideration and provide confdence bounds of DMRs

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