Aims

1) To familiarise students with computational approaches to tackling biological data handling and analysis
2) To introduce the concepts of algorithm design for molecular biology data
3) To develop skills in algorithm design with an emphasis on solving biological problems
4) To understand the most appropriate type of algorithms for differing analytical problems in molecular biology and to introduce some of the most appropriate implementation strategies.

Outline Of Syllabus

Biological data, levels of biological complexity, basic algorithm design, algorithms for sequence assembly, sequence similarity (heuristic versus dynamic programming; local versus global), sequence assembly, multiple sequence alignment (ClustalW), motif and profile assignment (HMMs), protein structure prediction, protein interaction prediction, algorithms for microarray data analysis, gene network construction, proteomics analysis and integrative network analysis.

Teaching Rationale And Relationship

Lectures will be used to introduce the basic material and the more advanced theoretical topics.
Practical sessions, mostly computer based, will give the student opportunity to build up skills in algorithm design development and implementation.

Assessment Rationale And Relationship

The exam will test the knowledge of novel and existing algorithm design, application and implementation.
The practical will test skills in algorithm design and implementation, planning skills and communication.

N.B. This module has both “Exam Assessment” and “Other Assessment” (e.g. coursework). If the total mark for either assessment falls below 35%, the maximum mark returned for the module will normally be 35%.