Statistical Methods for Functional Genomics
Course - Cold Spring Harbor, United States
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
(Courtesy of DNA methylation, via en.wikipedia.org)
Statistical Methods for Functional Genomics
Fri, Jun 21, 2013 - Wed, Jul 03, 2013
CSHL Campus, Cold Spring Harbor, United States
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