Computational Methods in Bioinformatics

The course can be part of the following programmes:

1. Computer Science, Master's Programme (N2COS)
2. Applied Data Science Master's Programme (N2ADS)
3. Computer Science, Bachelor's Programme (N1COS)
4. Mathematical Sciences, Master's Programme (N2MAT)

The course is a also a single-subject course at Gothenburg University.

To be eligible for the course, the student should have successfully completed 60 credits of studies in Computer Science, Software Engineering, Data Science, Mathematics, Mathematical Statistics, or equivalent. Furthermore, the student should have successfully completed a course in Programming (DIT013 Imperative Programming with Basic Object-orientation, DIT044 Object-oriented Programming, DIT143 Functional programming, or equivalent) and a basic course in discrete mathematic (DIT984, DIT857 or equivalent).

Applicants must prove knowledge of English: English 6/English level 2 or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.

This course demonstrates how computational methods that have been presented in other computing courses can be applied to solve problems in an application area. We look at problems related to the analysis of biological sequence data (sequence bioinformatics)
and macromolecular structures (structural bioinformatics).

Computing scientists need to be able to understand problems that originate in areas that may be unfamiliar to them, and to identify computational methods and approaches that can be used to solve them. Biological concepts needed to understand the problems will
be introduced.

Reading research articles is valuable training for scientists and researchers. Developing skill in reading research articles is useful preparation for future scientific studies, and at the same time their own scientific writing can be improved. Therefore, in this course,
research articles are used as the main reference material, in particular to show how to present ideas and methods, and how to critically evaluate them.

Computational methods and concepts featured in this course include: dynamic programming; heuristic algorithms; graph partitioning; image skeletonisation, smoothing and edge detection; clustering; sub-matrix matching; geometric hashing; constraint logic programming; Monte Carlo optimisation; simulated annealing; self-
avoiding walks.

Biological problems featured in this course include: sequence alignment; domain assignment; structure comparison; comparative modelling; protein folding; fold recognition; finding channels; molecular docking; protein design.

On successful completion of the course the student will be able to:

Knowledge and understanding

• describe and summarise problems that have been addressed in the bioinformatics literature, and computational approaches to solving them

Competence and skills

• design and implement computational solutions to problems in bioinformatics

Judgement and approach

• critically discuss different bioinformatics methods that address the same task or related tasks, and to discuss differences in the tasks adddressed, or differences in the computational approaches
• identify situations where the same computational methods are applied in addressing different problems, even across different application areas

Lectures and programming assignments.

Language of instruction: English

The course is examined by individual programming assignments and written assignments.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

Sub-courses

1. Assignments, 7.5 credits
   Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)

The grading scale comprises: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U).

To pass the course, all mandatory components must be passed. To earn a higher grade than Pass, a higher weighted average from the grades of the components is required.

The course is evaluated through meetings both during and after the course between teachers and student representatives. Further, an anonymous questionnaire is used to ensure written information. The outcome of the evaluations serves to improve the course by indication which parts could be added, improved, changed or removed.

The course is a joint course together with Chalmers.

It is recommended to have taken an introductory course in data structures beforehand. Familiarity with some basic chemistry concepts (including atoms and molecules, chemical bonding) is useful.

The course replaces the course DIT741 Computational Methods in Bioinformatics, 7.5 credits. The course cannot be included in a degree which contains DIT741. Neither can the course be included in a degree which is based on another degree in which the course DIT741 is included.