STATISTICAL METHODS
FOR FUNCTIONAL GENOMICS
June 21 - July 3, 2013
Application Deadline: March 15, 2013

Instructors:
Naomi Altman, Penn State University
Harmen Bussemaker, Columbia University
Sean Davis, National Cancer Institute
Olivier Elemento, Weill Cornell Medical College

Over the past decade, high-throughput assays have become pervasive in biological research due to both rapid technological advances and decreases in overall cost. To properly analyze the large data sets generated by such assays and thus make meaningful biological inferences, both experimental and computational biologists must understand the fundamental statistical principles underlying analysis methods. This course is designed to to build competence in statistical methods for analyzing high-throughput data in genomics and molecular biology.

Topics include:
• The R environment for statistical computing and graphics
• Introduction to Bioconductor
• Review of basic statistical theory and hypothesis testing
• Experimental design, quality control, and normalization
• High-throughput sequencing technologies
• Expression profiling using RNA-Seq and microarrays
• In vivo protein binding using ChIP-Seq
• High-resolution chromatin footprinting using DNase-Seq
• DNA methylation profiling analysis
• Integrative analysis of data from parallel assays
• Representations of DNA binding specificity and motif discovery algorithms
• Predictive modeling of gene regulatory networks using machine learning
• Analysis of posttranscriptional regulation, RNA binding proteins, and microRNAs

Format:
Detailed lectures and presentations by instructors and guest speakers will be combined with hands-on computer tutorials. The methods covered in the lectures will be applied to example high-throughput data sets.

Additional speakers last year included:
Sean Davis, Bruce Futcher, Tim Hughes, Nicholas Ingolia, Christina Leslie, Elaine Mardis, John Stamatoyannopoulos

This course may be supported with funds provided by:
National Institute of General Medical Sciences