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Statistical Analysis of Genome Scale Data

  December 06, 2011  
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
June 22- July 3, 2012

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.

Organized by: Naomi Altman, Harmen Bussemaker, Mark Reimers
Invited Speakers:

Speakers last year included:
Aedin Culhane, Dana Farber Cancer Institute
Sean Davis, National Institutes of Health
Olivier Elemento, Weill Cornell Medical College
Bruce Futcher, Stony Brook University
Kasper Hansen, Johns Hopkins University
Steve Horvath, University of California, Los Angeles
Wolfgang Huber, EMBL, Germany
Tim Hughes, University of Toronto, Canada
Jason Lieb, University of North Carolina, Chapel Hill
Robert Lucito, Cold Spring Harbor Laboratory
John Storey, Princeton University


Deadline for Abstracts: March 15, 2012





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