This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Undergraduate Handbook 2025-2026 (archived)

Module COMP3487: Bioinformatics

Department: Computer Science

COMP3487: Bioinformatics

Type	Level	Credits	Availability	Module Cap	Location
Open	3	10	Available in 2025/2026	None.	Durham

Pre-requisites

COMP2271 Data Science

Corequisites

None.

Excluded Combinations

None.

Aims

To introduce students to applications of Computer Science in Biology.
To introduce students to some important Statistical methods and algorithms.

Content

Dynamic programming algorithms for sequence alignment.
Efficient heuristic algorithms for sequence alignment.
Markov Chains and Hidden Markov Models (HMM).
Expectation-Maximisation algorithm with an application to parameter-estimation in HMM.
Phylogenetic Trees as a model of Evolution.
Maximum parsimony and character-based techniques for tree reconstruction.
Distance-based tree reconstruction via neighbour-joining techniques.

Learning Outcomes

Subject-specific Knowledge

On completion of the module, students will be able to demonstrate:
an understanding of the basic computational problems in Biology.
an understanding of some fundamental statistical techniques.
an understanding of basic tree-reconstruction algorithms.

Subject-specific Skills

On completion of the module, students will be able to demonstrate:
an ability to implement key algorithms within the area.
an ability to identify what methods are applicable to given Biological data.

Key Skills

On completion of the module, students will be able to demonstrate:
an ability to abstract out a computational problem from a real-world one.
an ability to solve a computational problem by an exact algorithm or a heuristic one.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Lectures enable the students to learn new material relevant to applications of Computer Science and Statistics in Biology.
Summative assessment assess the application of methods and techniques learned to solving computational problems in Biology.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total Hours
Lectures	22	2 per week	1 hour	22
Preparation and Reading				78
Total				100

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting	Resit Opportunity
Examination	100%	Online Examination	2 hours within 3 hour timed window	100%

Formative Assessment

Example formative exercises are given during the course.

Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.