Page 46 - Handbook PG 20182019

Faculty of Science Postgraduate Booklet, Session 2018/2019

3. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. & Stahel, W. A. (1986). Robust Statistics:
The Approached based on influence functions. John Wiley.
4. Rousseeuw, P.J. and Leroy, A. M. (1987). Robust Regression and Outlier Detection. John
Wiley.

SQB7018 Statistical Methods in Bioinformatics

Statistical modelling of DNA/protein sequences: Assessing statistical significance in BLAST using the
Gumbel distribution; DNA substitution models; Poisson and negative binomial models for gene
counts; Hidden Markov Model.

Algorithms for sequence analysis and tree construction: Dynamic programming for sequence
alignment and Viterbi decoding; neighbour-joining, UPGMA, parsimony and maximum likelihood
tree-building methods.

Analysis of high-dimensional microarray/RNA-Seq gene expression data: Statistical tests for
detecting differential expression, feature selection, visualization, and phenotype classification.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Computer Programming Linux OS

Humanity Skill:
TS5, LL3, LS3

References:
1. Jones, N.C. & Pevzner, P.A. (2004). An Introduction to Bioinformatics Algorithms.
Massachusetts: MIT Press.
2. Durbin, R., Eddy, S., Krogh, A. & Mitchison, G. (1998). Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids. Cambridge: Cambridge University Press.
nd
3. Ewens, W.J. & Grant, G.R. (2005). Statistical Methods in Bioinformatics: An Introduction. 2
Ed., New York: Springer.
nd
4. Pevsner, J. (2009). Bioinformatics and Functional Genomics. 2 Ed., New York: Wiley-
Blackwell.
5. Buffalo, V. (2015). Bioinformatics Data Skills. Sebastopol, CA: O’ Reilly Media.

SQB7019 Data Mining

Introduction to statistical methods and tools for analysing very large data sets and search for
interesting and unexpected relationships in data.

Data Measurement: Types of measurements, distance measure, data quality.

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