Undergraduate Programme and Module Handbook 2024-2025
Module COMP3487: BIOINFORMATICS
Department: Computer Science
COMP3487: BIOINFORMATICS
| Type | Open | Level | 3 | Credits | 10 | Availability | Available in 2024/2025 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- COMP2271 Data Science
Corequisites
- None.
Excluded Combination of Modules
- 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: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | No |
Formative Assessment:
Example formative exercises are given during the course.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University