BMS3025 : Bioinformatics
- Offered for Year: 2024/25
- Module Leader(s): Dr Phillip Aldridge
- Lecturer: Dr Amir Enshaei, Dr Lisa Russell, Dr Sarra Ryan, Professor Caroline Austin, Dr Daniel Rico Rodriguez, Dr Simon Cockell
- 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: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
Science is an ebb and flow of discovery and innovation. The scientific research community is experiencing a rapid increase in the size of the data sets our experiments generate. This has meant we have had to alter our data analysis skill-set to analyse, draw conclusions and present our studies in an understandable format.
A good example of where we as scientists are learning and continually improving our skill set is within the field of Bioinformatics. In this module we aim to examine the uses of various tools to analyse the large datasets we are now generating. We will also provide you with several case studies where these tools have been or are being exploited in research.
A key aspect of the underpinning skills required for bioinformatics is the ability to have confidence in exploiting a PC or MAC for more than our usual activities of document generation in MS Word, analysis of data using software such as MS excel and social media access.
• train students in using one analysis platform that is used across the bioinformatics sector
• explore the use of health informatics to improve the quality and safety of patient care
• examine the uses of various tools to analyse the large datasets
• generate an understanding, and an appreciation, of databases, workflows, and basic coding
• give the students the theory behind, and the practice in using, modern bioinformatic analysis tools
Outline Of Syllabus
The following topics and themes will be covered in this module:
• An introduction to the analysis platform R
• Impact of Next Generation Sequence Technologies on Science
• Impact of CHiP-seq and RNA-seq on experimental design
• Statistical analysis of large datasets as a mean to identify experimental robustness
• Importance of choosing the right data presentation style
• the use of computer code in data analysis
• the use of health informatics to improve the quality and safety of patient car
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 1 | 1:00 | 1:00 | In person - Introduction to the module |
| Scheduled Learning And Teaching Activities | Lecture | 8 | 1:00 | 8:00 | In person - Core conceptual material relating to module outcomes |
| Guided Independent Study | Assessment preparation and completion | 1 | 52:00 | 52:00 | Data analysis report |
| Scheduled Learning And Teaching Activities | Practical | 3 | 2:00 | 6:00 | In person - Computer sessions focusing on students used of R to analyse a data set |
| Scheduled Learning And Teaching Activities | Workshops | 5 | 1:00 | 5:00 | In person - computer sessions to develop students' familiarity with the software platform R |
| Guided Independent Study | Independent study | 1 | 28:00 | 28:00 | Writing up lecture notes, revision and general reading |
| Total | 100:00 |
Teaching Rationale And Relationship
In this module we will exploit the learning environment of the PC-clusters, to deliver interactive sessions that will act to introduce the students to the data analysis platform.
Eight lectures of material will be used to impart information in a concise manner, to highlight areas of importance and to interrelate with directed reading and self-directed study. These materials will be used to introduce a range of analysis strategies. We will use five PC-Cluster based workshops to introduce R to the students prior to the practical sessions.
The three practical sessions will provide the students with structured PC-Cluster based sessions in which they can explore and develop their understanding of R and in a structured environment, analyse the data set provided for the assessment and to gain valuable direct learning to explore the module content.
Private study will be used for self-directed learning, including further reading.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Case study | 1 | M | 100 | Report on the analysis of large sequence data-set - 1500 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 | Practical based questions within practical and workshop sessions for students to formatively assess understanding and progress. |
Assessment Rationale And Relationship
Summative Assessment: In the summative assessment the students will be presented with a large dataset which they have to analyse using the tools, ideas and approaches covered in the module. The assessment will explore their understanding of the material taught, as well as push their critical thinking and data-analysis skills. Their results will be presented as 1,500 word report, which will have to contain appropriate diagrams, tables and figures.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- BMS3025's Timetable