Sean Davis, University
of Colorado, Anschutz School of Medicine
Hans Tomas Rube, University
of California, Merced
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
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 ATAC-seq
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
