Level of Study
Autumn. First time autumn 2019.
Objectives and Content
The course gives an introduction to methods for analysis of biological sequences, beyond pairwise alignment, and for prediction and analysis of RNA and protein structures. Sequence-based methods covers multiple sequence alignment, estimation of phylogenetic trees, discovery and detection of sequence motifs, use of hidden Markov models in sequence analysis, and gene prediction. Structure-based methods includes predicting secondary structures in RNA and proteins and alignment of RNA and protein structures. The course focuses on the algorithms and methods but also includes practical application of relevant tools and databases.
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- Understands and is able to implement methods for aligning a set of biological sequences
- Understands and is able to explain methods for estimation of phylogenetic trees
- Understands and is able to implement methods for motif discovery and detection
- Understands and is able to utilize hidden Markov models for analysis of biological sequences
- Understands the main approaches to gene prediction and is able to implement and use selected algorithms for the same
- Understands representations and formats for describing structures of RNA and proteins
- Understands the main approaches for prediction of secondary structures in RNA and proteins and is able to implement and use selected algorithms for the same
The student can
- select and utilize appropriate tools on real biological data and to interpret resulting output
- Implement and adapt algorithms for analyzing sequences and predicting and comparing structures
The student is able to
- work in inter-disciplinary teams to address biological questions using computational approaches
Required Previous Knowledge
Recommended Previous Knowledge
BINF100 or corresponding background in bioinformatics and molecular biology. Be able to implement basic algorithms in a programming language of your own choice. A basic understanding of algorithms and efficiency is required. A basic course in statistics is highly recommended.
Credit Reduction due to Course Overlap
INF281: 5 credits
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.
Teaching and learning methods
The course is given as lectures and mandatory exercises
Lectures, 4 hours per week
Exercises, 2 hours per week
Compulsory Assignments and Attendance
Compulsory assignments are valid for 1 subsequent semester.
Forms of Assessment
The forms of assessment are:
- Exercises, 30 % of total grade.
- Written examination (3 hours), 70% of total grade.
All compulsory assignments must be approved before examination.
Examination Support Material
Non-programmable calculator, according to the faculty regulations
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.
T: 55 58 42 00
Spring 2020 written exams will not be arranged on campus. Please see course information on MittUiB.
Type of assessment: Written examination
- Withdrawal deadline
- Examination system
- Digital exam