The CRISPR-Cas9 technology allows the modification of virtually any gene in any organism of interest. It's generated a lot of interest, both in the research community and the general population.
One of the crucial components of CRISPR experiments is the design of the "guide RNAs" that will control where modifications occur. It's vital that the modification is made at the desired location and not elsewhere.
We developed a method utilising high-performance computing to efficiently assess the "off-target risk" of each guide RNA across entire genomes. We're seeking to expand our current method by providing more flexibility within its scoring system.
This project entails the application of high-performance computing to big datasets in order to achieve this.
This project will require you to apply your computer science skills in order to improve upon the existing method.
A starting point could be to consider how to include additional scoring approaches without causing performance losses, or to consider how to include these by virtue of finding performance gains elsewhere.
In this project, you'll review literature and existing bioinformatics pipeline in order to gain an understanding of what is currently available.
You'll have the opportunity to write multithreaded code in a low-level programming language in order to fully utilise high-performance computing hardware. During this, you will gain experience in working with a bioinformatics pipeline and have the opportunity to work alongside researchers working in the expanding field of bioinformatics.
Skills and experience
This project requires you to have strong programming skills. Some experience with low-level programming languages, such as C or C++, and writing multithreaded code would be beneficial but not required. Experience with (or willingness to learn) CUDA would also be ideal.
No prior experience with biology is necessary, but you must be willing to learn relevant concepts.
Contact the supervisor for more information.