Data Science for Genomics
There are three major subject areas of the course:
- Usage of R in applied statistics and data handling: This will be used throughout the course
- Visualization, handling and analysis of genomic data using the genome browser, the Unix command line and R
- Expression analysis using microarrays and DNA sequencing using R and public tools.
MSc Programme in Biochemistry
MSc Programme in Bioinformatics
MSc Programme in Biology
MSc Programme in Biology with minor subject
MSc Programme in Molecular Biomedicine
The student will achieve the following from attending the
After successfully completing the course, students will master the fundamentals of computational analysis of large biological datasets. This includes:
i) understanding the diverse laboratory techniques and biological processes generating the data
ii) understanding and mastering the statistical and informatics techniques used for visualization and analysis, including the selection of appropriate techniques for a given data and question
iii) interpreting analysis results in a biological context, and identify and apply follow-up analyses based on this.
The skill set taught in the course can be divided into:
- Applied statistics, visualization and data handling within R and the Unix command line
- Knowledge of molecular biology techniques that generate genomics data - cDNA analysis, ChIP, RNA-seq and more, and their strengths and weaknesses
- Visualization techniques for the data above: genome browsers and R
- Techniques for data mining and data exploration
There is a special focus on hands-on exercises to develop analysis skills; both within lessons, group work and in the final evaluation. We also have one day with speakers from industry that use similar techniques.
- To be able to analyze, visualize and interpret cutting edge biological data sets using biological and statistical toolsets combined.
- To solve realistic problems in which finding the appropriate methods - and the specific programming syntax necessary - for attacking sub-questions question is an important part of the problem.
Hybrid between lectures and computer exercises.
Students should have a molecular biology background
corresponding to those of students in Bioinformatics or Biomedicine
master programs (for instance "Molecular biology for non-life
students" in block 1 or a life-science oriented bachelor
education). Moreover, skills in statistics and R corresponding to
"Statistics for Molecular Biomedicine" in block 3 is
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Written assignment, 5 daysOral examination, 30 minutes (no preparation time)
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners/co-examiners
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Single subject courses (day)
- Class Instruction
- Practical exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
The number of seats may be reduced in the late registration period
- Study Board for the Biological Area
- Department of Biology
- Faculty of Science
- Albin Gustav Sandelin (5-636e646b7042646b71306d7730666d)
Are you BA- or KA-student?
Courseinformation of students