Unit information: Computational Genomics and Bioinformatics Algorithms in 2014/15

Unit name	Computational Genomics and Bioinformatics Algorithms
Unit code	EMATM0004
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Cristianini
Open unit status	Not open
Pre-requisites	An understanding of basic probability theory, and elementary programming skills are essential. Prior knowledge of Matlab is helpful but not essential.
Co-requisites	None
School/department	School of Engineering Mathematics and Technology
Faculty	Faculty of Engineering

Description including Unit Aims

The unit will focus on case studies of analysis of single genomes. It covers: gene finding; genome evolution; gene expression analysis; sequence alignment; hidden Markov models; phylogenics.

Aim:

To introduce students to practical tasks of genome analysis, based on real case studies.

Intended Learning Outcomes

1. Basic analysis of genomic data (DNA and AA sequences)
2. Gene finding, sequence alignments, inference of phylogenetic tree
3. Experience using real world data
4. Using standard online tools and datasets and databases
5. Reading scientific papers
6. Writing a professional report
7. Discussing the merits of different methods

Teaching Information

Lectures and computer laboratories

Assessment Information

100% coursework: all learning outcomes are assessed in 2 written projects (50% each) in which students undertake analysis tasks of real world biological data and report the results.

Reading and References

• Introduction to Computational Genomics: A Case Studies Approach

Nello Cristianini and Matthew W. Hahn Cambridge University Press, 2006 Hardback and Paperback (ISBN-13: 9780521856034 | ISBN-10: 0521856035