Module manager: Professor David Westhead
Email: d.r.westhead@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
A first degree (BSc Hons) in Biochemistry or equivalent Molecular biology subject area with some familiarity with LINUX based computer systems and use of the R statistical software package.
BIOL5274M High throughput Technologies (10 credits)
This module is not approved as an Elective
The module aims to provide an understanding of the use of high-throughput biomolecular data generation methods. The emphasis will be on understanding methods and the data that they typically give. Data analysis methods will be discussed but there will not be a strong emphasis on statistical methods that are covered in other modules. Some experience of data analysis will be provided.
The objectives of the module are to:
- Introduce students to a range of high-throughput experimental methods currently being used in biochemistry, molecular biology and genetics.
- Make students aware of data analysis methods and issues associated with the interpretation of high-throughput data.
- Allow students to appreciate the applications of high-throughput data generation to advance bioscience and medicine.
On completion of the module students will be able to:
1. Describe a range of currently important experimental methods which generate high-throughput data in molecular biology and genetics including how the work;
2. Apply and critique specific and generic data analysis methods associated with high throughput data;
3. Describe how high-throughput data can advance bioscience and medicine;
4. Analyse and interpret high-throughput data; and
5. Critically evaluate relevant research papers.
High-throughput DNA sequencing and applications in genome sequencing (genetics, cancer, rare diseases), epigenetics and genetic regulation (methylation, histone modification, DNA accessibility, ChIP-seq), gene expression studies (RNA-seq), studies of other RNA species (miRNA, lncRNA, circle RNA) and translation.
High-throughput proteomics and metabolomics using mass spectrometry, NMR and other methods. Chemical proteomics, high-throughput analysis of protein interactions.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practical | 4 | 3 | 12 |
| Seminar | 7 | 3 | 21 |
| Private study hours | 117 | ||
| Total Contact hours | 33 | ||
| Total hours (100hr per 10 credits) | 150 | ||
There are 4 practical sessions during the module where students work on computer data analysis exercise with the support of teaching staff and demonstrators. In these sessions they can obtain formative feedback on their work in preparation for the presentation assessment.
The unseen exam will involve summarising and critiquing a research paper based on the material delivered in the 7 seminar sessions. Summarising and critiquing papers is part of these seminars and provides formative feedback. For example, in the session on metabolomics students prepare and criticise a paper and then with tutor support study a published criticism of the same paper.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Presentation | Verbal presentation (12 minutes plus 3 minutes questions) | 40 |
| Total percentage (Assessment Coursework) | 40 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 3.0 Hrs Mins | 60 |
| Total percentage (Assessment Exams) | 60 | |
Written analysis of a scientific paper in essay format.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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