Analysis of biological sequences and structures

Undergraduate course

Course description

Objectives and Content

The course focuses on understanding and implementing 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, reconstruction of phylogenetic trees 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.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The student

  • Understands and is able to implement methods for aligning a set of biological sequences
  • Understands and is able to implement methods for reconstruction 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
  • Understands the Bayesian framework and likelihood in biological data analysis


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


General competence

The student is able to

  • work in teams to address biological questions using computational approaches



ECTS Credits


Level of Study


Semester of Instruction

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.

Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Assessment Semester
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
Reading List
The reading list will be available within July 1st for the autumn semester and December 1st for the spring semester.
Course Evaluation
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
Examination Support Material
Non-programmable calculator, according to the faculty regulations
Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser
Course Administrator
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.