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

Format:
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
Instructors:
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