At the end of the course, the student is able to:
- Analyze and Integrate omics data (i.e. Variant calling, Differential expression analysis, Protein quantification, metabolic pathway analysis, genome architecture integration, public data integration)
- Read and understand contemporary omics papers
- Understand the computational concepts in OMICs papers and translate these concepts to novel applications
- Understand the advantages and limitations of different omics techniques and their analysis
- Determine in the best type of analysis for the data at hand and propose concrete adaptations to tailor existing analysis methods to novel data types
- Perform omics data analysis using existing tools/packages and write tailored scripts to integrate multiple omics data types
Period: 1 May 2017 – 5 May 2017|
Joep de Ligt, Biomedical genetics/Genetics, 100%
Jeroen de Ridder, Biomedical genetics/Genetics, 10%
Edwin Cuppen, Biomedical genetics/Genetics, 10%
Berend Snel, Theoretical Biology and Bioinformatics, 10%
Invited speakers (differs between years), scheduled speakers listed below:
(Cuppen) Francis Blokzijl, UMC Utrecht, 10% (DNA)
(Veldink) Wouter van Rheenen, UMCU / Rudolf Magnus, 10% (RNA)
(Heck) Maarten Altelaar, Utrecht University, 10% (Protein)
(Verhoeven-Duif) Judith Jans, UMC Utrecht, 10% (Metabolic)
The correct analysis and integration of omics data has become a major component of biomedical research. The advances in technology have allowed for more sophisticated and unbiased approaches to assess the different omics data types. Large collaborative projects combined with databasing efforts have led to invaluable resources like ENCODE [https://www.encodeproject.org/], Expression Atlas [https://www.ebi.ac.uk/gxa/home], the Human Protein Atlas [http://www.proteinatlas.org/] and KEGG [http://www.genome.jp/kegg/]. These resources can provide valuable insights into your omics data and serve as a validation or quality control set when used appropriately. The challenge is to effectively analyze omics data and these large online resources after performing an experiment or getting clinical results.
For example, when analyzing tumors derived from a set of patients, the question is: how to correctly analyze your OMICs data and leverage public data by comparing these against your own data. The Cancer Genome Atlas alone numbers over 50,000 files from 3 different OMICs types. What are the correct and feasible strategies to utilize these data?
In this course a scientist (active within the respective OMICs field) starts the morning with a lecture, the accompanying scientific article will be available for prior reading. The presenter will introduce a recent study performed within their group and outline the data mining and data integration opportunities and issues they encountered. The lecture is followed by a discussion on how to conduct this research and possible approaches to expand on the current work or solve one of the encountered issues. Topics covered will include mutation analysis, expression profiling, protein abundance and metabolic pathways. In the afternoon students will be tasked with finding a solution to a challenge set by the presenter. Solving such problems can only be done through writing (small) computer programs and integrating relevant data sources.
This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introduction programming courses; having completed one of these is a hard prerequisite. Following the "Introduction to Bioinformatics for Molecular Biologists" course is highly recommended.
The goal of this course is to outline current omics analyses methods and the challenges and value of integrating public data in life science research. We will discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with OMICs data.
Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.
Please register online on the CS&D website: www.CSnD.nl/courses. CS&D students have priority in registration until 3 weeks before the start of the course. Maximum capacity is 25 participants.
Mandatory for students in own Master’s programme:
Optional for students in other GSLS Master’s programme:
Introduction to Python/R/ other programming language.