Are you interested in analyzing next-generation sequencing data but lacking in strong computational skills? In this skills track, geared towards non-computational biologists, you will learn to use Bioconductor, the specialized repository for bioinformatics software, along with essential Bioconductor packages. Then, you'll learn about current best-practice workflows for RNA sequencing differential expression analysis, as well as Chip-sequencing data.
Overview
Syllabus
- Introduction to Bioconductor in R
- Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
- RNA-Seq with Bioconductor in R
- Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
- Differential Expression Analysis with limma in R
- Learn to use the Bioconductor package limma for differential gene expression analysis.
- ChIP-seq with Bioconductor in R
- Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Taught by
Peter Humburg, Paula Martinez, James Chapman, John Blischak, and Mary Piper
