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Computational Genomics (MSc)
This programme is specifically designed to train the next generation of quantitative scientists and engineers to work in this exciting new field. With backgrounds ranging from mathematics, statistics, physics, computer science and engineering, graduates of this programme will learn advanced analytical techniques and gain practical experience in applying these techniques to genomics data. The genomics sciences have revolutionised our ability to explore at the molecular level every living organism on the planet. Using innovative and powerful algorithms, genomics scientists continue to identify and analyse the signals encoded in DNA sequences in areas as diverse as agriscience, evolutionary biology and precision medicine.
Why study this programme?
This course will focus on the use and development of algorithms and computational techniques to analyze and understand genomic data. Rapid advances in the technologies used to sequence DNA and RNA have led to an increase in the breadth of application of these sequencing-based genomics technologies, from fundamental scientific discovery in the life sciences to clinical applications in precision medicine. This requires a new generation of highly trained scientists who possess not only an understanding of the underlying biological principles and scientific technologies, but also the quantitative and computational skills necessary to analyze the large data sets generated using these cutting-edge genomics techniques.
Applications and Selections
PAC application code: GYS32
Selection is based on the candidate's academic record at an undergraduate level and their aptitude for the course.
Who Teaches this Course
Haixuan Yang, PhD
Aaron golden, PhD
Cathal Seoighe, PhD
Andrew Flaus, PhD
Derek Morris, PhD
Requirements and Assessment
Students are formally assessed through a variety of both continuous assessment and end-of-semester written examinations. Continuous assessment will include written assignments, programming exercises, genomic analyses, group and individual presentations, and case studies, while assessment of the Research Project includes examination of a written thesis, as well as oral presentations, and participation in a research seminar series.
Applicants must have achieved a second class honours degree or better in a discipline relevant to the MSc programme. Qualifying degrees include, (but are not limited to) mathematics, theoretical physics, physics, statistics, computer science and engineering (biomedical or electronic/electrical/computer engineering).
1 year, full-time
Next start date
A Level Grades ()
Please refer to the review/closing date webpage.
Next start date
Mode of study
The course comprises 90 credits; 60 credits will be obtained from taught modules and 30 from an individual research project. Students will undertake training in a bespoke molecular biology programme that reflects their prior studies in the quantitative or computational sciences. Students will also take a set of specialist modules including advanced techniques for genomics data analysis as well as modules on machine learning, data analytics and scientific visualization.
- Programming for biology
- Overview of molecular biology/genetics concepts
- Statistical computing in R
- Probabilistic models for molecular biology
- Algorithms for molecular biology
- Medical genomics I: genomics of rare and common diseases
- Medical genomics II: the cancer genome
- Genomics techniques I: sequencing library preparation
- Genomics techniques II: genomics data analysis
Optional modules include
- Scientific visualization
- Stochastic processes
- Machine learning
- Applied statistics
- Bayesian modelling
Why Choose This Course?
Who’s Suited to This Course
Related Student Organisations
Fees: Student levy
Fees: Non EU
For further infomation on postgraduate funding and scholarship opportunities see here
What Our Students Say
Alan Barnicle | R&D Scientist at Cambridge Epigenetix, Cambridge, U.K.
I was recruited by Cambridge Epigenetix in the U.K., a start-up genomics company backed by Google Ventures, just after completing my Ph.D. in Bioinformatics at NUI Galway. It's an exciting place to be as we are working at the frontier of technology development. My job as an R&D scientist covers a whole range of activities, from working on innovative genomics techniques used to study individual samples, to the development of new bioinformatics tools necessary to interpret the resulting data. The new Masters programme in Computational Genomics at NUI Galway provides exactly the sort of skillset that genomics scientists need in this highly dynamic and hugely rewarding career - particularly for those graduates who may have no formal prior experience of molecular biology, but whose computational/ mathematical skills will see them in high demand.