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Unit information: Computational Genomics and Bioinformatics Algorithms in 2018/19

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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 Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

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 details

Lectures and computer laboratories

Assessment Details

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

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