Stat Genetics Video - Methylation Analysis

Karen Conneely

Karen Conneely, PhD
Associate Professor
Emory University

Dr. Conneely received her Ph.D in Biostatistics from University of Michigan. Her research focuses on statistical methods for genetic association studies and large-scale studies of DNA methylation.  She is currently involved in GWAS, candidate gene studies, and methylation studies involving psychiatric outcomes, environmental stressors, and aging.  Through the development of novel techniques and adaptation of existing ones, her work seeks to identify biomarkers and variants involved in disease, and to explore the role of DNA methylation as a mediator between environment and phenotype.

 
  • Course Contents

Course Outline

Single-base resolution coverage offered by Whole Genome Bisulfate Sequencing(WGBS) has made it a gold standard for assaying DNA methylation. An higher resolution version of WGBS, but just enriched for CpG-rich regions, is the Reduced Representative Bisulfate Sequencing (RRBS). Analysis of WGBS and RRBS data involves use of several bioinformatics tools and statistical methods to perform a complete end-to-end pipeline starting from read alignment to the quantification of differentially methylated sites and regions. The first module of this video series will walk you through the different analysis strategies for quantifying differentially methylated sites from already aligned RRBS data. In order to compare and contrast the dense nature of WGBS with RRBS, latter modules were developed to show how a smoothing approach like Bsmooth algorithm can be used to generate high confidence differentially methylated regions.

Resources

Lecture Video

Analysis of whole-genome bisulfite sequencing data- 7th NHGRI Video

Published on December, 2017


Hand-on Video

Methylation Analysis using Bisulfite Sequencing Data- Module 1

Published on March, 2018

 

Methylation Analysis using Bisulfite Sequencing Data - Module 2

Published on March, 2018

 

Methylation Analysis using Bisulfite Sequencing Data - Module 3

Published on March, 2018

 

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