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Unit information: SWBio DTP: Statistics and Bioinformatics in 2021/22

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. Talas
Open unit status Not open



SWBio DTP: Data Science and Machine Learning for the Biosciences, SWBio DTP: Science in Society, Business and Industry, SWBio DTP: Rotation Project 1, followed by SWBio DTP: Rotation Project 2

School/department School of Biochemistry
Faculty Faculty of Life Sciences

Description including Unit Aims

This unit aims to deliver a working knowledge and understanding of the range of statistical and bioinformatic methods commonly used in biological science research, and how such methods are deployed in analyses of data.

Analyses of data, and in particular of large datasets, is becoming a fundamental technique common to many areas of biological science 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, an understanding of how and why each type of analysis can be deployed, and how to use 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.

Bioinformatic analyses of DNA sequence and other data is also an essential skill, be this for phylogentic, population genetic studies or gene expression analyses. 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 and Linux in such analyses, be able to identify the appropriate forms of analyses for their data, and be able to use these techniques to critically analyse relevant datasets.

Intended Learning Outcomes

To be able to:

  • Understand the diversity of different types of data and approaches to their analyses.
  • Understand R and how it can be used for descriptive statistics and graphing and in experimental design.
  • Design tests for association and difference - from basic (e.g. correlation, t-tests) to more advanced (e.g. regression, ANOVA).
  • Carry out and interpret statistical modelling, which may include general and generalised linear models, mixed models, and additive models (GLM, GLMM, GAM, GAMM).
  • Be aware of other methods, which may include multivariate (PCA, cluster), multi-model inference, and Bayesian analyses.
  • Have experience of using genomics approaches utilised in handling the output from massively parallel short read sequencing.
  • Effectively communicate and collaborate with bioinformaticians in the handling, modelling, and analysis of large-scale biological data.

Teaching Information

It comprises two intensive week-long periods of teaching (such as lectures, seminars, practical activities and workshops), each followed by a period of recommended and self-directed further reading and completion of assessment activities.

Assessment Information

There will be two assessments: (1) to demonstrate an understanding of the conceptual and practical aspects of statistical analyses by answering short answer-style statistical questions (50%), and (2) to demonstrate an understanding and competency in bioinformatic analyses by writing a bioinformatic practical report (50%).


If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BIOCM0010).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.