BMD2008 : Bioinformatics for Biosciences (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Dr Phillip Aldridge
- Co-Module Leader: Dr Simon Cockell
- Lecturer: Dr Laura Young, Dr Daniel Williamson, Dr Adam Wollman
- 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 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to:
Describe the growing importance of programming and its relevance to Biomedical Sciences
Introduce programming languages used in biomedical sciences
Demonstrate how biomedical scientists integrate the advantages of different programming languages to process, curate and analyse large data sets
Outline Of Syllabus
Topics to be covered in this module will include:
R its flexibility and limitations
Python – data extraction and curation
UNIX command line control of bioinformatic pipelines
Database construction and curation
Integration of programming languages in data analysis
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 8 | 1:00 | 8:00 | Lectures to introduce topics and concepts |
| Guided Independent Study | Assessment preparation and completion | 102 | 1:00 | 102:00 | Includes assessment preparation on completion of data analysis |
| Scheduled Learning And Teaching Activities | Practical | 18 | 2:00 | 36:00 | PC-cluster based sessions to learn and explore coding skills |
| Guided Independent Study | Skills practice | 10 | 1:00 | 10:00 | Independent study on coding skills using provided exercises |
| Guided Independent Study | Project work | 20 | 2:00 | 40:00 | Coding based data analysis of larger problems to include generate of dataset for assessments |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 4 | 1:00 | 4:00 | Non-compulsory, troubleshooting sessions to provide 1-to-1 guidance on coding and IT hardware problems related to assessments and module |
| Total | 200:00 |
Teaching Rationale And Relationship
Coding skills are like learning another language and takes time to be taught the principles andpractice their use. The rationale behind the teaching of this module reflects this with more emphasis on skill-based learning within PC-clusters and guided independent study through coding exercises provided.
Lectures will introduce concepts and topics in a formal environment to the student cohort.
Computational-based practicals will be the focus of our teaching methods, providing an environment in which will enable students to have hands-on experience of the necessary coding languages.
Problem based exercises relating to the computational-based practicals will be used to allow the students to further extend their knowledge and gain experience.
Non-compulsory drop-in session will be provided to enable students to approach the academic team to troubleshoot coding errors or IT hardware issues. These sessions will also enable to students to discuss their learning experience with the academic team.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 60 | Data report integrating a coding-based data analysis pipeline (max 1200 words) |
| Design/Creative proj | 2 | M | 40 | Graphical abstract detailing a data analysis pipeline and its output |
Assessment Rationale And Relationship
Two summative assessments will generate the framework to assess the learning outcomes, aimed at reinforcing core knowledge on the coding languages to be taught. These assessments will allow the students to ensure they have a full grasp of the core knowledge and help them to develop critical assessment of analysis pipelines.
The first in-course assessment (40%) will be a graphical abstract describing the integration of coding languages into an analysis pipeline. The output is expected to be visual highlighting the flow of the analysis and provide the code used. This will assess both understanding and application of bioinformatic methods, as well as the student’s potential to evaluate the visual output.
The second of two in-course assessments (60%) will be a written data report requiring the students to return a detailed narrative on an analysis pipeline, its implementation and the visual output. This assessment will provide the students a format to discuss the benefits and limitations of the analysis used. This will provide the students an opportunity to demonstrate their skill set developed through the module while also addressing the learning outcomes of being able to explain the pipeline used.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- BMD2008's Timetable