Title: "A classification approach for DNA methylation profiling with bisulfite next-generation sequencing data"
Speaker: Longjie Cheng; Department of Statistics, Purdue University

Place: Lilly (LILY) Hall G126
Date: September 30, 2014; Tuesday
Time: 4:30pm

With the advent of high-throughput sequencing technology, bisulfite-sequencing-based DNA methylation profiling methods have emerged as the most promising approaches due to their single-base resolution and genome-wide coverage. However, statistical analysis methods for analyzing this type of methylation data are not well developed. Although the most widely used proportion-based estimation method is simple and intuitive, it is not statistically adequate in dealing with the various sources of noise in bisulfite-sequencing data. Furthermore, it is not biologically satisfactory in applications that require binary methylation status calls.

We use a mixture of binomial model to characterize bisulfite-sequencing data, and based on the model, we propose to use a classification-based procedure, called the methylation status calling (MSC) procedure, to make binary methylation status calls. The MSC procedure is optimal in terms of maximizing the overall correct allocation rate, and the false discovery rate (FDR) and false non-discovery rate (FNDR) of MSC can be estimated. To control FDR at any given level, we further develop an FDR-controlled MSC procedure, which combines a local FDR-based adaptive procedure with the MSC procedure. Both simulation study and real data application are carried out to examine the performance of the proposed procedures.

Associated reading:
1. Lister, R. et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 462:315-322.

2. Cheng, L. and Zhu, Y. (2014) A classification approach for DNA methylation profiling with bisulfite next-generation sequencing data. Bioinformatics. 30(2):172-179.

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