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Unit information: Computational Genomics and Bioinformatics Algorithms in 2020/21

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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

An understanding of basic probability theory, and elementary programming skills are essential. Prior knowledge of Matlab is helpful but not essential.



School/department Department of Engineering Mathematics
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.


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

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets.

Assessment Information

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.

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