Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Faculty Handbook 2022-2023

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run each academic year.

Department: Computer Science

COMP3487: BIOINFORMATICS

Type Open Level 3 Credits 10 Availability Available in 2022/23 Module Cap None. Location Durham

Prerequisites

  • COMP2271 Data Science OR COMP2231 Software Methodologies

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 24 2 per week 1 hour 24
preparation and reading 76
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



If you have a query about a specific module or degree programme, please contact the appropriate department.

If you have a question about Durham's modular degree programmes, please visit our FAQ webpage. If you have a question about modular programmes that is not covered by the FAQ, or a query about the on-line Faculty Handbook, please contact us using the Comments and Questions form below.