Module manager: David Westhead
Email: d.r.westhead@leeds.ac.uk
Taught: Semester 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2024/25
Students are required to meet the programme entry requirements prior to studying the module.
None
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. Techniques covered will include whole genome/exome sequencing, gene expression, RNA-seq and epigenetics, proteomics, chemical proteomics, high-throughput RNA biology, single-cell methods and metabolomics. Data analysis methods will be discussed but there will not be a strong emphasis on statistical methods that are covered in other modules.
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 successful completion of the module students will be able to:
1. Describe of a range of currently important experimental methods which generate high-throughput data in molecular biology and genetics;
2. Explain how these high-throughput methods work;
3. Demonstrate an understanding of the specific and generic data analysis methods and issues associated with high throughput data;
4. Describe how high-throughput data can advance bioscience and medicine;
Indicative content for this module includes:
1. Introduction to DNA sequencing
2. Introduction to RNA sequencing and Epigenomics
3. Proteomics
4. Translatomics
5. Single Cell Methods
6. Proteomics
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 2 | 12 |
| WEBINAR | 1 | 1.5 | 1.5 |
| WEBINAR | 5 | 1 | 5 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
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).
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.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Essay | 2,000 words | 70 |
| Reflective log | 1,000 words | 30 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 18/11/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team