Computer Laboratory – Course material 2008

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Bioinformatics

Lecturer: Dr P. Liò

No. of lectures: 12

Aims

This course focuses on algorithms used in Bioinformatics and System Biology. Most of the algorithms are general and can be applied on multidimensional and noisy data from other fields. All the necessary biological terms and concepts useful for the course and the examination will be given in the lectures.

Lectures

Introduction to Bioinformatics. Primer on molecular biology and ``omics''. Open problems and types of data.
Sequence alignment I. Dynamic programming. Global and local alignment algorithms. Scoring matrices.
Sequence alignment II. Block alignment and four Russians speedup. Multiple alignment.
Database search. Comparing a sequence against a database. Blast family, PatternHunter, Using spaced seeds.
Algorithms for evolutionary trees I. Parsimony methods.
Algorithms for evolutionary trees II. Distance methods.
Algorithms for evolutionary trees III. Maximum Likelihood methods; bootstrap test.
Information theory. Markov properties of genome sequences.
Applications of Hidden Markov Models in Bioinformatics. Examples on gene and protein structure prediction.
Microarray data analysis. Steady state and time series microarray data. Clustering methods.
Introduction to system biology I. Algorithms for generating genetic networks from microarray data. Identifying regulatory elements using microarray data.
Introduction to system biology II. Analysis of Genetic and biochemical networks; Gillespie family of algorithms.

Objectives

At the end of this course students should

understand Bioinformatics terminology
be able to work with with bioinformaticians and biologists
have some experience of the data used in Bioinformatics
master the most important algorithms in the field

Recommended reading

* Jones N.C. & Pevzner, P.A. (2004). An introduction to bioinformatics algorithms. MIT Press.
Felsenstein, J. (2003). Inferring phylogenies. Sinauer Associates.

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