| Coordinator: |
Prof Cathal Seoighe
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| Lectures: |
24 x 1 hour lectures in Semester 2, plus
labs and tutorials.
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| Credits: |
9 ECTS
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| Content: |
Probabilistic models and their application to molecular biology. The course will begin with an introduction to
DNA and amino acid sequences and a review of the theory and algorithms for Markov Chains, Markov Processes and Hidden Markov Models. Applications of Hidden Markov Models to several problems in bioinformatics will be considered, including sequence alignment, gene finding, and protein-family annotation. The course will also cover the use of continuous-time Markov Processes to model molecular sequence evolution including phylogenetic models.
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| What you will learn: |
How probabilistic models are applied in bioinformatics and how to implement these models on a computer.
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| Why take this course: |
In recent years biology has seen a flood of data from whole genomes to microarray technologies that allow the expression levels of all genes to be measured at once. These developments generate many opportunities to apply mathematical
and statistical techniques to problems with biomedical significance. This course will help you to gain an
appreciation for how the mathematician, computer scientist or statistician can approach problems in contemporary bioinformatics research.
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| Assessment: |
Homework, computer labs, and written exam paper at
end of semester |
| Texts: |
Durbin et al. Biological Sequence Analysis . probabilistic models of proteis and nucleic acids .
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| Prerequisites:
| Good background in probability theory. Preferably some exposure to probability models (such as Markov Chains).
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