Module overview
This module is only compulsory for the MSc Genomics (Informatics) pathway, and optional for other pathways.
This module builds on the knowledge and experience gained from the Genomic Technologies and Basic Informatics module, introducing the Linux command line environment. Students will perform analysis of data generated by different omic technologies, particularly transcriptomic and cancer genomic data. Upon completing this module students will be in a strong position to base their MSc research project on NGS data.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Apply appropriate tools for NGS data analysis to identify potentially pathogenic variants, including somatic driver mutations
- Create basic scripts and pipelines for the automated analysis of NGS datasets in the Linux environment
- Apply appropriate tools for splice-aware cDNA sequence alignment, quantification of various aspects of transcription (for example gene, exon, transcript abundance) and differential gene expression analysis
- Design and apply appropriate machine learning approaches to analyse complex and high dimensional biological datasets
- Develop strategies to prioritise candidate genes from differential gene expression analysis for further study
Syllabus
This module will cover topics including the Linux command line and important commands; using command line tools for data pre‑processing; principles of sequencing tumour–normal pairs to identify somatic mutations; annotation of somatic variants using multiple databases such as ClinVar and COSMIC; principles of RNA sequencing to determine gene expression profiles; identifying known and novel transcripts, quantifying expression, and performing differential expression analyses at multiple levels (genes, exons and transcripts) using appropriate software; understanding the benefits and limitations of popular machine‑learning methods applied to complex and high‑dimensional biological datasets; introduction to pathway analysis, including basic tools for network analysis, network visualisation and modelling of biological processes.
Learning and Teaching
Teaching and learning methods
A variety of learning and teaching methods will be adopted to promote a wide range of skills and meet the differing learning needs of the group.
The teaching will include seminars, practical demonstrations, discussions and exercises surrounding interpretation of data and biomedical scenarios, and specialist lectures given by a range of academics. This will ensure a breadth and depth of perspective, giving a good balance between background theories and principles and practical experience.
| Type | Hours |
|---|---|
| Assessment tasks | 35 |
| Teaching | 28 |
| Independent Study | 87 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Assessment
Assessment strategy
The assessment for the module provides you with the opportunity to demonstrate achievement of the learning outcomes. In addition to the summative assessments, during the course of the module there will be opportunities to obtain feedback in the form of unassessed, formative activities. The pass mark for this module is 50%; if you have failed the module, the Board of Examiners may offer you the opportunity to submit work at the next referral (resit) opportunity. You will only be required to refer components where you have not achieved the pass mark, and marks for components which were passed will be carried forward. For the data analysis project only, this referral will be a resubmission of the initial report, improved incorporating feedback. The final module mark will be capped at 50%.Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Workshop activities
- Assessment Type: Formative
- Feedback: Individual feedback.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Written assignment | 50% |
| Data analysis project | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Written assignment | 50% |
| Data analysis project | 50% |
Repeat Information
Repeat type: Internal & External