NBIK20004U Advanced Bioinformatics for Next-Generation Sequencing
MSc Programme in Bioinformatics
The course covers advanced topics in Bioinformatics for analysis of data next-generation sequencing (NGS). The course is based mostly on scientific papers and texts. The topics include:
- Mapping algorithms
- Population genetic and medical genetic analysis based on NGS
- Cross-species comparative genomic analyses
- Other topics relating to high-throughput DNA sequencing
The student will know and understand the main methods used in the course. More specifically, the student will obtain knowledge of:
- Analysis of high-throughput sequencing data
- Genetic analyses
- Phylogenetic construction
- Genome evolutionary analyses
The student will be able to:
- Do the types of data analyses covered in the course
- Interpret results of such analyses in a biological context
- Explain the methods covered in the course
- Select the appropriate methods and tools for a given problem covered in the course
The student will obtain or improve these general competences:
- Will be able to discuss and explain methods in bioinformatics 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 bioinformatics
It is not allowed to pass both courses.
The course is prepared for the bioinformatics graduate programme, but it is open to third year BSc students and MSc students in general.
- Practical exercises
- 7,5 ECTS
- Type of assessment
- Continuous assessmentMandatory homework assignments. Grade based on the average number of points obtained in the assignments.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Renewed hand in of mandatory homework assignments.
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.