HUM-MOLGEN events: Statistical Methods for Functional Genomics

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 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.