12-16 March 2018
High throughput (HT) techniques such as transcriptomics or metabolomics are of great significance in many areas of biology. However, the path from a boring list of differentially
expressed genes to a biological understanding of the results is not straightforward.
This course offers computational techniques that go beyond a simple technical or statistical analysis. It covers techniques for the analysis of gene set enrichments, pathway analysis, gene ontologies, functional analysis of metabolomic profiling and making use of correlations and coexpression networks. A prominent part of the course will be devoted to data visualization and visual data exploration.
The students will gain the ability to independently process and analyse HT data sets, select the appropriate tools, functionally interpret the results as well as learn the paradigms of computational biology and statistics which will allow them to efficiently communicate with computational biologists.
As an incentive, each student will receive a set of gene expression profiles for a different organism, and during the course they will use these to generate species-specific gene expression modules and test their utility. If we are successfull, we will attempt a joint publication.
The course is aimed at researchers moving the first steps in epigenomic data analysis and / or interested in learning more about this subject. The course will offer a balanced mixture of lectures and hands-on practical tutorials using popular tools and R/BioConductor packages. Previous knowledge of genomics data formats from Illumina sequencers and exposure to bioinformatics is beneficial but not a necessary prerequisite.
On each day, the course will consist of four parts:
• Lecture: theoretical introduction to the days focus
• Hands-on guide: guided practical session in R where students replicate the analysis performed by the teacher. While the lecture is general, here specific R techniques and R packages are introduced
• Guided self-study: students are given excercises and problems to solve and work on them individually under the guidance of the teacher
• Individual project work: each student will receive a transcriptomic (RNASeq or microarray) data set to analyse throughout the course
• Lecture: wrap-up and side notes; preparation for the following day
Eisenacher Str. 1, 10777 Berlin
• Day 1: Introduction to statistical reasoning and R
– Lecture: "Statistics gone wrong: basics of statistical problems in HT applications"
– Hands-on guide: working with R: first steps
– Guided self-study: using R for data loading and basic statistical calculations
– Individual project work: loading data for the individual project
– Lecture: "So you have a list of thousand gene names: why do we do HT analyses?"
Dr. January Weiner studied biology and mathematics at the Jagiellonian Univerity in Cracow, graduating in experimental evolutionary biology in 1996. He then moved to microbiology and transcriptomics, and received his Ph.D. from the University of Heidelberg for work on the transcriptome of Mycoplasma pneumoniae. Later, he worked on evolution of proteins in the bioinformatic group of Erich Bornberg-Bauer, and habilitated in 2009 in the area of evolutionary biology. Since 2009 he works at the Max Planck Institute for Infection Biology on high-throughput biomarkers in tuberculosis.
Course material and refreshments
Course material, refreshments, lunch and accommodation
430 € (VAT included)
695 € (VAT included)
Please click HERE to get all the information about our packages.
Application deadline is February 12th, 2018
> 30 days before the start date = 30% cancellation fee
< 30 days before the start date= No Refund.
Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.