COMP0082 Bioinformatics

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session

2018-19

Module

Bioinformatics

Code

COMP0082

Module delivery

1819/A7U/T2/COMP0082 Undergraduate

Related deliveries

1819/A7P/T2/COMP0082 Postgraduate

Prior deliveries

COMPM058

Module leader

Jones, David

Contributors

Jones, David

Bryson, Kevin

Module administrator

Ball, Louisa

Aims

The overall aim of this module is to introduce students to the new field of bioinformatics (computational biology) and how machine learning techniques can be employed in this area. The module is aimed at students who have no previous knowledge of biology and so the aim of Part 1 of the course is to give a basic introduction to molecular biology as a background for bioinformatics.Part 2 will concentrate on modern bioinformatics applications, particularly those which make good use of pattern recognition and machine learning methods.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. have a basic knowledge of modern molecular biology and genomics;
  2. understand the advantages and disadvantages of different machine learning techniques in bioinformatics and how the relative merits of different approaches can be evaluated by correct benchmarking techniques;
  3. understand how theoretical approaches can be used to model and analyse complex biological systems.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MEng Computer Science (International Programme) (year 4)
  • MEng Computer Science (year 4)
  • MEng Mathematical Computation (International Programme) (year 4)
  • MEng Mathematical Computation (year 4)

Prerequisites:

In order to be eligible to select this module, student must:

  • also have selected Supervised Learning (COMP0078) ; and
  • be familiar with the principles of techniques such as neural networks, Support Vector Machines, and Hidden Markov Models.

Content

Part 1: Basic molecular biology (6 lectures)

  • Introduction to Basic Cell Chemistry: Cell chemistry and macromolecules. Biochemical pathways e.g. Glycolysis. Protein structure and functions.
  • Cell Structure and Function: Cell components. Different types of cell. Chromosome structure and organisation. Cell division.
  • The Hereditary Material: DNA structure, replication and protein synthesis. Structure and roles of RNA. Genetic code. Mechanism of protein synthesis: transcription and translation. Mutation.
  • Recombinant DNA Technology: Restriction enzymes. Hybridisation techniques. Gene cloning. Polymerase chain reaction.
  • Genomics and Structural Genomics: Genes, genomes, mapping and DNA sequencing.

Part 2: Bioinformatics Applications (3 lectures per subject)

  • Biological Databases: Overview of the use and maintenance of different databases in common use in biology.
  • Case study: the CATH database of protein structure.
  • Gene Prediction: Methods for analysing genomic DNA to identify genes. Techniques: neural networks and HMMs.
  • Detecting Distant Homology: Methods for inferring remote relationships between genes and proteins. Techniques: dynamic programming, HMMs, hierachical clustering.
  • Protein Structure Prediction: Methods for predicting the secondary and tertiary structure of proteins. Techniques: neural networks, SVMs, genetic algorithms and stochastic global optimization.
  • Transcriptomics: Methods for analysing gene expression and microarray data. Techniques: clustering, SVMs.
  • Agent-based Genome Analysis: Automation of genome analysis using intelligent software agents.
  • Drug Discovery Informatics: Approaches to drug discovery using bioinformatics techniques.

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures, tutorials, seminars, written and programming exercises.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

85

 

2

Coursework

15

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.