NBIA05008U Biological Sequence Analysis
Volume 2014/2015
Education
MSc Programme in
Bioinformatics
MSc Programme in Biology
MSc Programme in Biology
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
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.
Academic qualifications
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.
Teaching and learning methods
Lectures (4-5 per week),
exercises (2-4 hours per week) and homework (one per
week).
Remarks
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).
Workload
- Category
- Hours
- Exam
- 20
- Lectures
- 40
- Practical exercises
- 24
- Preparation
- 122
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutesOral examination (30 minutes) with preparation time (30 minutes).
- 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
demonstrate an excellent fulfillment of the learning outcome
described above.
Course information
- Language
- English
- Course code
- NBIA05008U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- A (Tues 8-12 + Thurs 8-17)
- Course capacity
- 50 students
- Continuing and further education
- Study board
- Study Board of Biomolecular Sciences and Technology
Contracting department
- Department of Biology
Course responsibles
- Anders Krogh (6-6a747b787071496b727837747e376d74)
Saved on the
04-12-2014