Bioinformatics of High Throughput Analyses

Course content

There are three major subject areas of the course:

  1. Usage of R in applied statistics and data handling: This will be used throughout the course
  2. Visualization, handling and analysis of genomic data using the genome browser, the Unix command line and R
  3. Expression analysis using microarrays and DNA sequencing using R and public tools.
Education

MSc Programme in Bioinformatics
MSc Programme in Biology
MSc Programme in Biology with minor subject
MSc Programme in Molecular Biomedicine
 

Learning outcome

The student will achieve the following from attending the course:

Knowledge:

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.

Skills:

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, microarrays 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.

Competences:

  • 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.

See Absalon.

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 necessary.

Academic qualifications equivalent to a BSc degree is recommended.

Written
Oral
ECTS
7,5 ECTS
Type of assessment
Written assignment, 5 days
Oral examination, 30 minutes (no preparation time)
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Aid
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)

  • Category
  • Hours
  • Lectures
  • 32
  • Class Instruction
  • 3
  • Preparation
  • 60
  • Practical exercises
  • 31
  • Project work
  • 60
  • Exam
  • 20
  • English
  • 206