MMB8052 : Bioinformatics for Biomedical Scientists
- Offered for Year: 2024/25
- Module Leader(s): Dr Simon Cockell
- Lecturer: Dr Maria Duenas Fadic, Dr Amir Enshaei, Professor Joris Veltman, Professor David Elliott, Dr Louise Reynard, Mr George Merces
- Owning School: Biomedical, Nutritional and Sports Scien
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to provide a practical introduction to commonly-used informatics technologies for data analysis in the life sciences. Bioinformatics is increasingly integral to the pursuit of molecular biology research. As laboratory methods become more data intensive, a practical knowledge of how this data is handled, processed and analysed is essential to deriving full value from these techniques. This module will principally focus on the application of bioinformatics skills and will provide a grounding in the computational competencies required to work with data generally and modern molecular biology datasets particularly. By the end of the module, students should be able to work with a range of informatics technologies, design and undertake their own analysis of a complex dataset, and use the results of that analysis to synthesize a biological discussion.
Outline Of Syllabus
The following topics and themes will be covered in this module:
• Computing for bioinformatics
• Principles of sequence alignment
• High Throughput Sequencing (HTS) applications
• The handling and analysis of HTS data
• Running bioinformatics analysis at the Linux command line
• The R statistical programming framework and Bioconductor
• Statistical analysis of high throughput data using R
• Data visualisation techniques
• Practical approaches to pathway analysis
• Machine Learning and Artificial Intelligence in bioinformatics
In addition, lectures on the module will provide case studies on the outcomes of bioinformatics work in a number of key areas, including genomics, proteomics, transcriptomics and epigenomics.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Present in person lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 50:00 | 50:00 | Preparation and submission of practical lab report |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation for the practical exercise |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | Preparing notes and from lectures and reading |
| Scheduled Learning And Teaching Activities | Practical | 10 | 3:00 | 30:00 | Present in person practicals |
| Guided Independent Study | Skills practice | 10 | 3:00 | 30:00 | Consolidation of practical skills |
| Guided Independent Study | Independent study | 1 | 40:00 | 40:00 | Reflective Learning and Reading |
| Total | 200:00 |
Teaching Rationale And Relationship
The core of the module is the practical sessions – these will introduce students to the core tools central to modern bioinformatics, and will demonstrate their use with a variety of modern biological data sets. The practicals will build on each other into a coherent body of knowledge enabling students to apply a range of tools to a complex dataset. Peer support will be encouraged through short group exercises within the practical framework.
The lectures are intended to supplement this material by demonstrating to students the end-products of this computational process – therefore the lectures will be case studies from researchers who make significant use of bioinformatics in their research and will show students how the practical techniques they are learning are actually used in the research process.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 25 | Practical exercise to computationally determine answers to short answer questions |
| Practical/lab report | 1 | M | 75 | Practical lab report, particularly focussing on methods and data visualisation. Max 2000 words. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | Unassessed quiz to test understanding of methodologies introduced in practicals. |
Assessment Rationale And Relationship
The module is practical-led, and the assessment outcomes are rooted in the application of this practical knowledge. The Short Answer Test summative assessment tests the ability of the students to directly apply the practical techniques being demonstrated through the taught sessions. A series of short questions will be posed, and the answers should be derived automatically by computation. The resulting script which produces the answers is the subject of the assessment, rather than just the answers themselves.
The final assessment is a write-up of the final practical exercise, which will assess the students’ ability to design an analysis approach, apply the appropriate tools and interpret the outputs.
The formative assessment provides students to test their understanding of methodologies introduced in practicals.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MMB8052's Timetable