| Unit name | Omics |
|---|---|
| Unit code | BIOLM0031 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Barker |
| Open unit status | Not open |
| Pre-requisites |
Scientific programming, Statistics and R. |
| Co-requisites |
None. |
| School/department | School of Biological Sciences |
| Faculty | Faculty of Life Sciences |
This unit will introduce students to the theoretical and practical aspects of statistical approaches to analysing biological data. These include the design of analyses and use of computational tools to analyse omics data. The students will have the opportunity to plan, develop and implement their own analyses to answer a wide variety of biological questions using omics data.
The aim of this unit will be to:
1. Provide students with a detailed understanding of the concepts behind designing and performing omic analyses.
2. Provide students with the skills required to use and interact with the pieces of software require to perform such analyses.
The Learning Outcomes (LOs) for this unit are:
A: Knowledge and Understanding:
1. to understand the theory, rationale and limitations of `omics experiments.
2. to develop knowledge on the different data types from different omics experiments and how these data can be manipulated and exploited.
B: Intellectual Skills/Attributes:
1. to design to high throughput `omics experiments as appropriate to address given hypotheses.
2. to identify and deploy computational and statistical analysis methods with appropriate parameters to extract usable results from `omics experiments.
C: Other Skills /Attributes (Practical/Professional/Transferable):
An approach and mindset which will enable students to:
1. Design and analyse robust experiments in areas outside of `omics or biology
2. Integrate new software and methods into their future work whilst retaining overall integrity of and confidence in their analyses and findings.
The unit will be delivered through a mixture of short lectures followed by individual exercises with computers. Blackboard will be used to engage students with the unit content.
A summative computer assessment will consist of a final computer task integrating all the learning objectives.
RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Mathematical and Computational Biology)
