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COMP3487: BIOINFORMATICS

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Type	Open
Level	3
Credits	10
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Computer Science

Prerequisites

COMP2271 Data Science

Corequisites

None.

Excluded Combinations 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
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.

More information

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