Module 08776 (2001)
Syllabus page 2001/2002
06-08776
Algorithms & Methods for Bioinformatics
Level 2/I
Peter Coxhead (coordinator)
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
None.
Outline
This module will provide an introduction to the information content of biological sequence data and the computational algorithms used to analyse such data.
Aims
The aims of this module are to:
- provide an introduction to the information content of biological sequence data
- introduce the computational algorithms used to analyse such data
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | demonstrate an understanding of biological sequence data and the methods used to analyse it | Examination |
| 2 | use proprietary software to solve simple sequence alignment problems | Coursework |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs lectures, tutorials, practicals per week
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 1 hr examination (50%), coursework (50%). Resit by examination only.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Beginning Perl for Bioinformatics | Tisdall, J. | O'Reilly, 2001 |
| Bioinformatics Computer Skills | Gibas, C. & Jambeck, P. | O'Reilly, 2001 |
| Algorithms on Strings, Trees and Sequences | Gusfield, D. | Cambridge University Press, 1997 |
| Bioinformatics -- the Machine Learning Approach | Baldi, P. & Brunak, S. | MIT Press, 1998 |
Detailed Syllabus
- Genomes -- diversity, size and structure.
- Database quality and redundancy.
- Proteins and proteomes; protein length distributions.
- Introduction to perl and its application to simple problems in bioinformatics.
- Information theory; information content of biological sequences; information reduction; prediction of functional features; sequence logos; complexity, pattern and periodicity as properties of simple sequences.
- Prediction of molecular function and structure.
- Sequence comparison and alignment problems; the Smith-Waterman algorithm; the Needleman-Wunch algorithm; substitution and gap scores; global and local alignment; alignment scores; substitution matrices; gap scores; Monte Carlo statistical methods for alignment evaluation; database search methods; vector-based comparison; consensus word methods; template methods, progressive alignment; pairwise comparison; statistically-based methods; consensus sequences; weight matrices; masking methods for database search.
- If time permits, further topics such as the following may be introduced. Hidden Markov Models (HMMs) and their applications. Probabilistic models of evolution; trees; substitution probabilities and evolutionary rates; data likelihood; optimal trees.
Last updated: 21 January 2001
Source file: /internal/modules/COMSCI/2001/xml/08776.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus