9, 11, 13, 16, 17, 18 November 2026

To foster international participation, this course will be held online

This course will provide biologists and bioinformaticians with practical  statistical analysis skills to perform rigorous analysis of high-throughput  genomic data. The course assumes basic familiarity with genomics and with R  programming, but does not assume prior statistical training.  It covers the statistical concepts necessary to analyze genomic and transcriptomic  high-throughput data generated by next-generation sequencing, including:  hypothesis testing, data visualization, genomic region analysis, differential  expression analysis, and gene set analysis.

●             Modern Statistics for Modern Biology (by Holmes and Huber)

●             The Bioconductor 2018 Workshop collection

** Day1 – Introduction (Nov 9, 3-6 PM, Berlin time)

- learn how to import text (e.g., 'comma-separated values') files into R,
- learn how to perform manipulations of R data frames,
- learn how to apply R functions for statistical analysis and visualization,
- learn how to use R packages to extend basic functionality
- learn to create high-quality graphics with base R plot & ggplot2

** Day2 – Hypothesis testing (Nov 11, 3-6 PM, Berlin time)

- get familiar with the statistical machinery of hypothesis testing, its vocabulary, its purpose, and its strengths and limitations
- learn how to conduct frequently used tests in R and how to interpret the results

** Day3 - Introduction to Bioconductor (Nov 13, 3-6 PM, Berlin time)

- learn how to use rtracklayer::import() to read genomic files
(e.g. BED, GTF, VCF, FASTA) into Bioconductor objects
- learn how to work with genomic region data (exons, genes, ChIP peaks, copy number variants, …) in Bioconductor
- learn how to find regions of overlap between experimentally-derived genomic regions (eg. CNV coordinates provided as BED) and functional genomic regions (eg. gene coordinates provided as GTF)

** Day4 - RNA-seq data analysis (Nov 16, 3-6 PM, Berlin time)

- get familiar with core concepts and key terminology of RNA-seq analysis
- understand the essential concepts of read mapping, counts computation, normalization and differential expression analysis and get to know selected R/Bioconductor packages for that purpose (Rsubread, EDASeq, edgeR, DESeq2)

** Day5 - Differential expression analysis (Nov 17, 3-6 PM, Berlin time)

- understand what multiple testing means
- understand the false discovery rate and how to apply it to a vector of p-values in R
- learn how to conduct a differential expression analysis with DESeq2 and how  to interpret the results
- understand similarities and differences to differential expression analysis with edgeR or limma/voom

** Day6 - Gene set analysis (Nov 18, 3-6 PM, Berlin time)

- understand what are gene sets and pathways,  and what are major resources for obtaining them (GO / KEGG)
- understand the basic statistical concepts underlying GO/KEGG overrepresentation analysis
- learn how to perform permutation-based gene set enrichment analysis using the EnrichmentBrowser package

• "I really enjoyed the course. The instructor has extensive experience and answered my questions in depth."
• "The course helped me understand the overall workflow of RNA-seq analysis and how to use Bioconductor tools effectively. The hands-on sessions were especially useful for connecting theory with practice."
• "I enjoyed it very much. The introduction to core Bioconductor data structures and workflows was particularly valuable."

1 - RNAseq for beginners - ONLINE, 20th-28th of May

2 -Developing R/Bioconductor packages - ONLINE, 6-10 July

3 - MicroRNA annotation and small RNA-Seq Analysis - ONLINE, 6-9 October

4- Single-cell RNAseq with R/Bioconductor - ONLINE, 16-20 November 

5 - Exploring and Visualizing Omics Data with iSEE  - 25-26 November