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ST416: Advanced Topics in Biostatistics

∗∗∗ Please note that this module will not be running in the 2018/19 academic year. ∗∗∗

3 lectures per week over 10 weeks. This module will run in Term 2.

Assessment: 100% by 2 hour examination

Prerequisite(s): ST111/112 Probability A and B, ST218/219 Mathematical Statistics A&B, basic computing literacy (R, Matlab,...)

Aims: This module presents the application of methods from probability, statistical theory, and stochastic processes to problems of interest to bioinformaticians and systems biologists, mainly in the area of biosequence analysis.

Objectives: It is expected that students who have taken the module will have mastered the basic set of ideas required in order to carry out further research in bioinformatics methods and algorithms, or to apply these ideas in biomedical applications.


• Single DNA sequence analysis:
- Signal modelling
- Pattern analysis

• Multiple DNA/protein sequence analysis:
- Detailed study of pairwise alignment algorithms and substitution matrices

- a detailed study of the algorithm and underlying theory

• Hidden Markov models:
- Forward-Backward algorithm and parameter estimation
- Applications to protein family modelling, sequence alignment and gene finding

• Gene Expression, Microarrays and Multiple Testing:
- differential expression – one gene and multiple genes

• Evolutionary Models:
- Discrete-Time Models
- Continuous-Time Models

• Phylogenetic Tree Estimation:
- Modelling, Estimation and Hypothesis Testing

Illustrative Bibliography:
1. Statistical Methods in Bioinformatics - An Introduction, by W. J. Ewens and G. R. Grant (Springer-Verlag New York, Second Edition, 2005)
2. Biological Sequence Analysis by R. Durbin, S. Eddy, A. Krogh, G. Mitchison Cambridge University Press, 1998, ISBN: 0 521 62971 3
3. Computational Genome Analysis: An Introduction by Michael S. Waterman, Simon Tavare, Richard C. Deonier , Springer Verlag , 2005

You may also wish to see:

ST416: Resources for Current Students (restricted access)