Biological Sequence Analysis
Course 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, including deep learning approaches
- Hidden Markov models with applications
- Motif finding and discovery
- Mapping and assembly of DNA sequencing data
- The knowledge and skills acquired in this course will enable the students to understand and develop state-of-the-art algorithms.
MSc Programme in Biochemistry
MSc Programme in Bioinformatics
MSc Programme in Biology
MSc Programme in Biology with a minor subject
MSc Programme in Molecular Biomedicine
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
Lectures (4-5 per week), exercises (2-4 hours per week) and homework (one per week).
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.
Academic qualifications equivalent to a BSc degree is
recommended.
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 to class (contact teacher if not possible).
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 30 minutes (30-minute preparation time)
- Aid
- Written aids allowed
The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted during the preparation time.
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
-
Same as the ordinary exam
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
- 40
- Preparation
- 141
- Practical exercises
- 24
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NBIA05008U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 1
- Schedulegroup
-
A
- Capacity
- 60
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student. - Studyboard
- Study Board for the Biological Area
Contracting department
- Department of Biology
- Department of Nutrition, Exercise and Sports
Contracting faculty
- Faculty of Science
Course Coordinator
- Amelie Stein (12-69756d74716d367b7c6d7176486a717736737d366c73)
Teacher
Anders Krogh
Jeppe Vinther
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