2024/25 Taught Postgraduate Module Catalogue

OGDS5200M Analytical Skills in Precision Medicine

15 creditsClass Size: 150

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

Taught: 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable

Year running 2024/25

Pre-requisite qualifications

Students are required to meet the programme entry requirements prior to studying the module.

Module replaces

None

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 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 datasets in academia, industry and the NHS;
- Enable students to develop independent learning skills including the use of real-world data based assignment.

Learning outcomes
On successful completion of the module students will be able to

1. Demonstrate understanding of genes, their function and their association with disease;
2. Identify mutations in clinical settings and query databases of known natural genetic variation;
3. Evaluate potential disease-causing changes to protein structure and function;
4. Perform relevant statistical tests to identify associated variables;
5. Reduce the dimensionality of a genomic dataset and interpret the results; identify clusters in a given dataset;
6. Identify significant features and visualise important patterns from genomic datasets.

Syllabus

Indicative content for this module includes:

1. Gene function
2. Gene variation
3. Gene variants and disease
4. Functional annotation and biological context
5. PCA and Cluster Analysis
6. Metagenomics

Teaching methods

Delivery type	Number	Length hours	Student hours
Discussion forum	6	2.00	12.00
WEBINAR	1	1.50	1.50
WEBINAR	5	1.00	5.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Private study

Across each week of learning students will actively engage with pre-prepared teaching and learning resources which scaffold learners to achieve learning outcomes (independent online learning). Each week follows a set pattern of acquiring knowledge which is then applied to a substantive activity which will usually be authentic to real-world application. Weekly asynchronous discussions (such as discussion boards) allow for peer-to-peer and peer-to-tutor discussion which supports completion of the substantive activity. At the end of each week of learning students consolidate their learning through reflective activities and a weekly live webinar session with the module tutor. Each unit also provides students with the opportunity for exploration and self-directed learning as is expected at masters level (private study).

Opportunities for Formative Feedback

- The module’s digital learning materials provide regular opportunities for participants to check their understanding and gain feedback (e.g., case studies with short answer questions and automated feedback, MCQs with detailed feedback on correct/incorrect answers).

- The individual unit webinars and discussion forums provide opportunities for formative feedback from peers and tutors.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	1,000 word practical report	50.00
Assignment	1,000 word practical report	50.00
Total percentage (Assessment Coursework)		100.00

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

Reading list

The reading list is available from the Library website

Last updated: 18/11/2024

Disclaimer

Browse Other Catalogues

• Undergraduate module catalogue
• Taught Postgraduate module catalogue
• Undergraduate programme catalogue
• Taught Postgraduate programme catalogue

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD