NBIA05008U Biological Sequence Analysis

Volume 2018/2019
Education
  • MSc Programme in Bioinformatics
  • MSc Programme in Biology
  • MSc Programme in Molecular Biomedicine
  • MSc Programme in Biochemistry

  • MSc Programme in Biology with a minor subject

Content

The course covers the fundamental theoretical background for biological sequence analysis as well as applications of the methods, which are learned through homework and exercises. The topics include

  • Alignment methods for biological sequences (DNA, RNA, proteins)
  • Methods for searching sequence databases
  • Hidden Markov models with applications
  • Motif finding and discovery
  • Mapping and assembly of DNA sequencing data
Learning Outcome

Knowledge:

The student will:

  • know the field of biological sequence analysis
  • know and understand the main algorithms and methods used in
    • pairwise and multiple alignment
    • searching of sequence databases
    • hidden Markov models of biological sequences
    • finding and discovery of motifs in biological sequences
    • mapping and assembly of DNA sequencing data
  • understand the biological contexts in which to apply biological sequence analysis


Skills:

The student will be able to:

  • derive simple probabilistic methods and algorithms for biological sequence analysis
  • explain the algorithms and methods covered in the course
  • interpret results of standard methods used in biological sequence analysis
  • select the proper sequence analysis methods and tools for a given biological problem
  • run some standard computer programs for biological sequence analysis


Competences:

The student will obtain these general competences:

  • Will be able to discuss and explain methods in biological sequence analysis with researchers in bioinformatics and related sciences
  • Will be able to contribute to interdisciplinary projects involving biological sequence analysis
  • Will be able to read, understand, and discuss scientific literature in biological sequence analysis
Literature

See Absalon.

Knowledge within molecular biology is recommended (can be obtained simultaneously). Programming at the level of "Linux and Python Programming" is recommended (can be taken simultaneously). Some skills in math and statistics are an advantage.
Lectures (4-5 per week), exercises (2-4 hours per week) and homework (one per week).
The course is mandatory in the bioinformatics graduate programme, but it is open to third year BSc-students and MSc-students in general. Participants are expected to bring a laptop equipped with a network card to class (contact teacher if not possible).
  • Category
  • Hours
  • Exam
  • 1
  • Lectures
  • 40
  • Practical exercises
  • 24
  • Preparation
  • 141
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Preparation time 30 minutes.
Aid
Written aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Re-exam

As ordinary.

Criteria for exam assesment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.