2024/25 Taught Postgraduate Module Catalogue

BIOL5327M Analytical Skills in Precision Medicine

15 creditsClass Size: 30

Module manager: Dr Sergei Krivov
Email: s.krivov@leeds.ac.uk

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

Year running 2024/25

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.

Co-requisites

BIOL5178M	High-Throughput Technologies
MATH5741M	Statistical Theory and Methods

Module replaces

BIOL5322M - Analytical Skills in Precision Medicine

This module is not approved as an Elective

Module summary

This module will train participants to evaluate and use DNA and protein sequences and structure. The emphasis will be on the use of computational tools to gain information about genes and variants, their function and associations with disease, as well as predicting protein structure. 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. In addition students will learn be introduced to multivariable analysis of data (e.g., Principal Component Analysis and K-means clustering). It will demonstrate how these techniques can be applied to gene expression data, to reduce the dimensionality of the data, reveal clinically relevant clusters, perform relevant statistical tests to identify associated variables and interpret the results.

Objectives

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;
Develop independent learning skills, enhancing employability through the use of teamwork, and improve oral and written communication skills, 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. study genes and their relevance to human disease using database resources and genome browsers.
2. utilise multiple computational tools to investigate the effects of genetic variation on protein structure and function.
3. perform relevant statistical tests to identify associated variables
4. reduce the dimensionality of a genomic dataset and interpret the results; identify clusters in a given dataset
5. identify significant features and visualise important patterns from genomic datasets

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

Syllabus

This module will cover the following three 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 variables.

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

Bioinformatics:
- 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.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	1.00	10.00
Practical	7	3.00	21.00
Seminar	6	1.00	6.00
Private study hours			113.00
Total Contact hours			37.00
Total hours (100hr per 10 credits)			150.00

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

Coursework

Assessment type	Notes	% of formal assessment
Report	Open-ended exercise, where a student will analyse a given data set, using all the tools learned (2,000-3,000 words)	70.00
Group Project	A group based work, where students are required to answer a specific research question, e.g. find the best biomarker for a given data set (800 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: 24/09/2024

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