Unit name | SWBio DTP: Statistics and Bioinformatics |
---|---|
Unit code | BIOCM0010 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Morgan |
Open unit status | Not open |
Pre-requisites |
None |
Co-requisites |
SWBio DTP: Statistics and Bioinformatics, SWBio DTP: Science in Society, Business and Industry, SWBio DTP: Research Project 1, SWBio DTP: Research Project 2 |
School/department | School of Biochemistry |
Faculty | Faculty of Life Sciences |
This 20 credit point unit aims to deliver a working knowledge and understanding of the range of statistical and bioinformatic methods commonly used in life science research, and how such methods are deployed in analysis of data. It comprises two intensive week-long periods of classroom-based learning, each followed by a period of recommended and self-directed further reading and completion of assessment activities.
Analysis of data, and in particular of large datasets is becoming a fundamental technique common to many areas of biological research and it is therefore important that those entering the profession are familiar with such techniques, even if they are not directly relevant to their current research projects. The unit will provide students with a thorough grounding in the types of statistical tests that are available, and with an understanding of how and why each type of analysis can be deployed, using R scripts to analyse data. It will include discussion of the limitations of each approach and the types of data to which each is appropriate. An appreciation of these limitations is essential if experiments are to be designed in an appropriate manner.
Analysis of DNA sequence data (Bioinformatics) is also an essential skill, be this for phylogentic, population or gene expression analysis. This part of the unit will focus on how to manipulate such data and then to analyse such datasets in a meaningful manner, and will include working in a Linux environment.
On completion, the student will have acquired familiarity with the terminology in common usage within these forms of analysis, be confident in using R or Linux in such analysis, able to identify the appropriate forms of analysis for their data and to be able to use these techniques to critically analyse relevant datasets.
To be able to:
Lectures, seminars, practical activities and workshops.
Student Input
A total of 200hrs as follows:
Statistics:
Bioinformatics:
These assessments span the full range of the approaches and methods taught in the unit.
All marks will be moderated by the Unit Director.
Bioinformatics:
UNIX and Perl to the Rescue!: A Field Guide for the Life Sciences (and Other Data-rich Pursuits) Paperback – 19 Jul 2012
by Keith Bradnam (Author), Ian Korf (Author)
Paperback: 425 pages
Publisher: Cambridge University Press (19 July 2012)
Language: English
ISBN-10: 0521169828
ISBN-13: 978-0521169820