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2022/23 Taught Postgraduate Module Catalogue

BIOL5322M Analytical Skills in Precision Medicine

20 creditsClass Size: 60

Module manager: Dr Sergei Krivov

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2022/23

Pre-requisite qualifications

As per programme requirements:
BSc 1st class or 2i, or equivalent (for example a pass in MBChB, BDS), in a relevant scientific discipline which would normally be one of the biological sciences but a natural sciences graduate will be considered. Subject to University regulations MBChB or BDS students who had completed 3 years of study would be eligible to intercalate.
A-level mathematics (or equivalent level of numeracy demonstrated through undergraduate degree or other relevant experience) will also be required.


BIOL5178MHigh-Throughput Technologies
MATH5741MStatistical Theory and Methods

This module is not approved as an Elective

Module summary

In the analysis of health and genomic data, it is vital to identify patterns in order to gain valuable insights from the data. This module will develop analytical and computational skills to discover, describe and exploit patterns in such data.Computational biology databases and tools that aid in the interpretation and understanding of biomedical research results and their placement within the wider context of the field will be explored. This will include skills in programming, information retrieval, assimilation and dissemination. Students will also be exposed to the legal, ethical and professional frameworks that are relevant to research data use. Students will be provided with real-world case-based projects in data science to work with during this module and will develop analytical, teamwork and independent learning skills, all of which will provide a strong foundation for the dissertation, further research and in employment.


The objectives of the module are to:
- Provide students with analytical and computational skills to enable them to analyse and interpret large biomedical datasets
- Expose students to some of the current applications of these data sets in academia, industry and the NHS;
- Give students a solid grounding in the legal, ethical and professional guidelines that are relevant to research data
Develop independent learning skills, enhancing employability through the use of teamwork, and improve oral and written communication skills, through a series of mini-assignments, including the use of real-world case-based projects in data science.

Learning outcomes
On completion of the module, the student should be able to:
1. Perform relevant statistical tests to identify associated variables; reduce the dimensionality of a genomic dataset and interpret the results; identify clusters in a given dataset; identify significant features and visualise important patterns from genomic datasets.
2. Analyse and manipulate large datasets using Python, R, and their extension packages.
3. Evaluate and use multiple computational tools and databases appropriate to the study of DNA and protein.
4. Analyse the similarities and differences between and within the relevant laws, ethical principles and professional guidelines, synthesising these into defensible, practical frameworks that can be applied to real-world research data settings.
5. Critically evaluate key information from written and verbal scientific communications; summarising and synthesising scientific material in writing and verbally.

Skills outcomes
Computational tools for programming and analysis - Python and Biopython; R and Bioconductor packages.


This module will cover the following four key areas:
Statistical analysis of large biomedical data sets:
- Introduction to vectors, matrices, eigen vectors, matrix decomposition, and covariance and correlation matrices.
- Principal Component Analysis (PCA) and clustering such as K-means clustering.
- Statistical tests to identify associations between variable.

Computational tools for programming and analysis:
- Python and Biopython
- R and Bioconductor packages

- Databases and genome browsers: getting information about genes, their function and their association with disease
- Genetic variation: identifying mutations in the clinical setting and querying databases of known natural (and disease causing) genetic variation.
- Assigning function: determining the potential effect of a genetic variant of unknown clinical significance
- Protein structure: resources for inspecting potentially disease-causing changes to protein structure, and function.
Legal and ethical principles in data management:
Legal, ethical and professional guidelines of relevance to research data use including Data Protection Law, confidentiality and privacy, the principles of medical/research ethics and relevant GMC/NMC codes of practice.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Practical (computer based)63.0018.00
Industry Site Visit / Seminars16.006.00
Independent online learning hours30.00
Private study hours113.00
Total Contact hours57.00
Total hours (100hr per 10 credits)200.00

Private study

Independent online learning: The students will learn R and Python using self-directed learning guides, supported by staff during introductory lectures and two associated clinics/practical sessions. Specific milestones for achievement will be built into the guides and student progress against these will be measured during the clinics. This method of delivering R training is currently being used in the School of Mathematics and will be adopted for this programme, extending the same training method to Python.
Private study: this includes the time associated with background reading for each lecture/seminar, and the time associated with the preparation and completion of the two assessments.

Opportunities for Formative Feedback

Monitoring will be during the supervised practical sessions and clinics during which students will be expected to show completed worksheets and summarise their progress.

Methods of assessment

Assessment typeNotes% of formal assessment
ReportOpen-ended exercise, where a student will analyse a given data set, using all the tools learned (4,000 words)70.00
Group ProjectA group based work, where students are required to answer a specific research question, e.g. find the best biomarker for a given data set (500 words).30.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 06/09/2022


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