This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 681
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #307979
Title: Genome-Wide Detecting Differential Epigenetic/Genetic Changes Using Next-Gen Sequencing Data
Author(s): Weichun Huang*+ and Nicole V. Jordan and Amy N. Abell and Gary L. Johnson and Leping Li
Companies: National Institute of Environmental Health Sciences and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and National Institute of Environmental Health Sciences
Address: , RTP, NC, 27709,
Keywords: hypothesis testing ; comparative analysis ; ChIP-seq ; mRNA-seq ; epicenter
Abstract:

The cost-effective Next-Gen sequencing technologies have been increasingly used for identifying genome-wide epigenetic/genetic changes. The relative new type and huge volume of data from these technologies, however, pose informatics challenges that are unmet by the existing methods. We described a simple hierarchical model of statistical tests for genome-wide detecting differential changes between different samples. The key features of our model are 1) accounting for large variability of read depth of coverage without reducing the statistical power of detecting changes, 2) providing multiple data normalization methods adapting to be used in different scenarios, 3) adjusting multiple testing automatically by controlling false discovery rate or family-wide type I error. Results from testing on both real histone ChIP-seq and mRNA-seq data suggested our method was effective.


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