Of potential interest: Statistical Methods for Functional Genomics June 23 - July 6, 2016 Application Deadline: March 31 2016
http://bit.ly/CSHL_Data_2016 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 and reproducible research • Review of basic statistical theory and hypothesis testing • Expression profiling using RNA-seq and microarrays • Single-cell approaches, including single-cell RNA-seq • Chromatin profiling using ChIP-seq, DNase-seq, and ATAC-seq • Epigenomics, including DNA methylation profiling • Systems genetics and eQTL analysis • Representations of DNA binding specificity, motif discovery algorithms • Predictive modeling of gene regulatory networks using machine learning • Analysis of post-transcriptional regulation by RNA binding factors Feel free to contact me if you have specific questions. Please feel free to forward to any and all interested parties. Sean
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