- Simon Anders (Institute for Molecular Medicine, Helsinki)
- Jennifer Bryan (University of British Columbia, Vancouver)
- Vincent J. Carey (Channing Laboratory, Harvard Medical School)
- Wolfgang Huber (European Molecular Biology Laboratory (EMBL), Heidelberg, HD-HuB)
- Michael Love (Dana Farber Cancer Institute and the Harvard School of Public Health)
- Martin Morgan (Roswell Park Cancer Institute, Buffalo, New York)
- Charlotte Soneson (University of Zurich)
- Levi Waldron (CUNY School of Public Health at Hunter College, New York)

Teaching Assistants:
- Simone Bell (EMBL, Heidelberg, HD-HuB)
- Alejandro Reyes (EMBL, Heidelberg, HD-HuB)
- Mike Smith (EMBL, Heidelberg, HD-HuB)

10-07-2016 – 15-07-2016

University of Padova, Bressanone-Brixen, Italy

The one-week intensive course teaches statistical and computational analysis of multi-omics studies in biology and biomedicine. It covers the underlying theory and state of the art (the morning lectures), and practical hands-on exercises based on the R / Bioconductor environment (the afternoon labs). The course covers the primary analysis of high-throughput sequencing based assays in functional genomics and integrative methods including efficiently operating with genomic intervals, statistical testing, linear models, machine learning, bioinformatic annotation and visualization.

The planned topics include:

- Introduction to Bioconductor
- Elements of statistics: hypothesis testing, multiple testing, regression, linear models, regularization, clustering and classification, visualization, and experimental design
- Reproducible research with Rmarkdown and knitr
- Computing with sequences and genomic intervals
- Working with annotation – genes, genomic features and variants
- RNA-Seq data analysis and differential expression    
- Single-cell RNA-Seq
- Working with ChIP-seq data
- Version control with Git
- Improving performance with code parallelization

Learning goals:
At the end of the course, you should be able to run analysis workflows on your own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.

The course is intended for researchers who have basic familiarity with the experimental technologies and their applications in biology, and who are interested in making the step from a user of bioinformatics software towards adapting or developing their own analysis workflows.

The four practical sessions of the course will require you to follow and modify scripts in the computer language R. As such, participants are required to bring their own laptop with the most recent release versions of R and Bioconductor installed.  Installation help will be provided at the venue before the course starts.

R, Bioconductor, Genomics, Statistics, Multi-omics

R & Bioconductor

Mike Smith
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