General provisions

The course is offered as part of the Master's programs in the Faculty of Medicine.

Entry requirements

Admitted to the Master's Programme in Medical Research

or

undergraduate education of 180 credits within life sciences (e.g. biomedicine, biotechnology, medicine, veterinary medicine or equivalent) including at least 10 credits each of cell biology and biochemistry; additionally, 7.5 credits in genetics at Master's level are required.

Proficiency in English equivalent to the general entry requirements for first-cycle (Bachelor's level) studies.

Learning outcomes

On completion of the course, the student should be able to:

• Work in a UNIX/LINUX operating system, including manipulation of files and directories, working with text files, performing basic system administration tasks, installing bioinformatics software/tools, writing shell scripts, manage jobs on desktop computers and servers. Understand how to develop UNIX/LINUX skills.
• Understand principles for using scripting (Perl/Python or similar) for handling large biological datasets, including how to store, process and sort data. Understand how to develop scripting skills.
• Perform standard analyses of Next Generation Sequencing data, including variant calling, RNAseq, de novo assembly. Understanding of NGS platforms including advantages and limitations. Use of NGS data files and formats. Understand and design NGS workflow steps from raw data. Perform quality control, mapping, visualisation, and downstream analysis. Use relevant bioinformatics software and tools for analysis of NGS data understand advantages and limitations of each tool. Deposit and retrieve NGS data from public databases (e.g. NCBI).
• Use of R for statistical data analysis, including data import/export, summary statistics, graphics, statistical testing, and installing packages. Understand how to develop skills in R.
• Perform standard linkage/association (QTL/GWAS) analyses. Be able to use common analysis software and create required input data files and formats using scripting. Understand the underlying modeling assumptions of the most commonly used analysis approaches. Interpret obtained results and understand the advantages and limitations of linkage vs association analysis to identify candidate genes for Mendelian and complex traits.
• Bioinformatic functional prediction based on non-synonymous amino-acid substitutions. Deleteriousness and conservation scores. Variant annotation and effect prediction. Understanding of experiments involved in ENCODE project to determine genome function (i.e. transcription factor bind sites, methylation, chromatin structure) and comparative genomics to determine genome function and how to incorporate these into data analysis.
• Demonstrate an understanding for metabolomics and proteomics data analysis.

Content

The course utilises current research problems to illustrate different statistical and bioinformatics data analysis methods used for genomics data and their applications in studies of human genetics, model organism biology and natural variation and evolution. Data that are analysed include those from: large scale genetic polymorphism data from next generation sequencing and SNP-chip genotyping, RNAsequencing, genotype to phenotype associations, and functional prediction from sequence data. Students will gain proficiency in the entire data analysis process from installation of software to efficient summarization of results using advanced graphics. The course gives insight in the central role of statistical and bioinformatics analysis in current genomics and other omics research and experience in using state-of-the art methodologies within the analysis of such data. The course covers working in a UNIX/LINUX command line environment, scripting using Perl/Python, statistical data analysis with applications in R, processing and analysis of next-generation sequence data of various types, and analysis and interpretation of results in genomics research.

Instruction

The teaching is performed as lectures, mandatory seminars and workshops in English.

Assessment

Examination includes a written exam graded fail (U) or pass (G). The bioinformatics problem solving ability will be examined by practical assignments relating to each of the course sections, that are to be solved individually or in groups, and that will be graded fail (U) or pass (G) only. To pass the course the students have to successfully complete all practical assignments and pass the written exam. Possibility for completion of not approved practical assignments may be given at the earliest at next course and only in case of a vacancy. Students who have failed the first examination are allowed five re-examinations.

If there exist special reasons the examiner can give allowance for alternative sets of assessment to examine an individual student. Specific conditions may e.g. be special pedagogic support described by the university's coordinator for special support.