NBIA07023U Bioinformatics of High Throughput Analyses

Volume 2015/2016
Education

MSc Programme in Molecular Biomedicine
MSc Programme in Bioinformatics

MSc Programme in Biology

Content

There are four major subject areas of the course:

 

  1. Introduction to the program R and 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 galaxy tool and R
  3. Expression analysis using microarrays and DNA sequencer data (”tag data”) using R and public tools
  4. Analysis of proteomics data using R and public tools.
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

 

  1. An introduction to the R statistical language
  2. Applied statistics, visualization and data handling within R and the Galaxy web tool
  3. Knowledge of molecular biology techniques that generate genomics data - cDNA analysis, ChIP, RNA-seq, microarrays, mass spec and more, and their strengths and weaknesses
  4. Visualization techniques for the data above: genome browsers and R
  5. 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-questons question is an important part of the problem.

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, a basal statistics course such as "Statistics for Molecular Biomedicine" in block 3 is strongly recommended.
Hybrid between lectures and computer exercises.
  • Category
  • Hours
  • Colloquia
  • 3
  • Exam
  • 20
  • Lectures
  • 32
  • Practical exercises
  • 31
  • Preparation
  • 60
  • Project work
  • 60
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Written assignment, 1 week
The final exam is an individual larger written end-of-course homework. Students are given 1 week to finish it.
Exam registration requirements

In order to be allowed to the final exam, the student must have had three smaller written group projects approved.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners/co-examiners
Re-exam

Written homework as the ordinary exam. The three smaller written group projects have to be approved before taking a re-exam.

Criteria for exam assesment

To obtain the grade 12:

  • The student must be able to explain the motivation, biological relevance and use of the methods covered in the course.
  • The student must be able to understand and critically assess relevant scientific literature.
  • The student must demonstrate expertise in the tools used in the course.
  • The student must be able to suggest which methods and programs to apply for a given biological problem, and to point out problems and difficulties relating to such applications.
  • Analogously, the student must be able to understand the strengths and weaknesses of different biological data types.
  • The student must, with the help of program documentation and lecture material, be able to identify the methods that are appropriate and the syntax necessary for solving problems.
  • The student must be able to after analysis interpret the analysis outcome in a biological setting, and identify and apply relevant follow up-analyses or extensions.