NBIA05008U Biological Sequence Analysis

Volume 2013/2014
Education
MSc Programme in Bioinformatics
Content
  • Pairwise alignment of biological sequences (DNA, RNA, proteins)
  • Searching sequence databases
  • Multiple alignment
  • Hidden Markov models with applications
  • Motif finding and discovery
Learning Outcome
Knowledge:
The student will
  • know the field of biological sequence analysis
  • know and understand the main algorithms and methods used in biological sequence analysis
  • 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
  • interpret results of standard methods used in biological sequence analysis
  • select the proper sequence analysis tools for a given biological application of sequence analysis


Competencies:
The student will obtain these general competences
  • Will be able to discuss and explain methods in biological sequence analysis with researchers in bioinformatics and biological sciences
  • Can contribute to interdisciplinary projects involving 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).
Lectures (4-6 per week) and exercises (2-4 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
  • 20
  • Lectures
  • 40
  • Practical exercises
  • 25
  • Preparation
  • 121
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Oral examination
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Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assesment
In order to achieve the grade 12 the student must be able to
  • explain the motivation, biological relevance and use of sequence analysis methods covered in the course
  • present and explain the mathematical and algorithmic details of the methods covered in the course
  • apply selected sequence analysis programs on biological data
  • explain how the programs work and advanced parameter settings and other details for several of these programs
  • suggest which methods and programs to apply for a given biological problem and to point out problems and difficulties relating to such applications