Statistical Methods for Functional Genomics

High-throughput genomics assays have become pervasive in modern biological research. To properly interpret these data, experimental and computational biologists need to have a firm grasp of statistical methodology. This course is designed to build competence in quantitative methods for the analysis of high-throughput molecular biology data.

Topics include:
• Review of R and introduction to Bioconductor
• Review of statistical methods for genomics
• Microarray technologies
• High-throughput sequencing technologies
• Basic analysis (quality control, normalization)
• Analysis using predefined gene sets
• Cis-regulatory sequence analysis
• Modeling of transcriptional networks
• DNA methylation assays and DNase I footprinting
• Expression profiling by RNA-Seq
• Analysis of ChIP-chip and ChIP-Seq data
• Integration of multiple data types
• Expression QTL analysis

Detailed lectures and presentations by guest speakers in morning and evening will be combined with hands-on computer tutorials in the afternoon. The methods covered in the lectures will be applied to public high-throughput data sets, primarily human, mouse and yeast data. Students will be expected to have a basic familiarity with the R programming language at the start of the course.
+ show speakers and program
Naomi Altman, Penn State University
Harmen Bussemaker, Columbia University
Sean Davis, National Cancer Institute
Olivier Elemento, Weill Cornell Medical College
Mark Reimers, Virginia Commonwealth University

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

21 May - 3 Jun 2013
Cold Spring Harbor
United States of America
meeting website