PhD Study : FPGA Computational Acceleration in Genomics – bringing personalised medicine closer to clinical practice for healthy communities

Summary

The era of whole genome sequencing (WGS) using high-throughput sequencing (HTS) technologies has arrived and is pouring out gigabases of reads (sequences) in a day. The wealth of knowledge held within this sequencing data brings with it the promise of true personalised medicine. Technological advances of HTS technologies have been pivotal in accelerating DNA sequencing and lowering the cost per genome. However, to fully benefit from such a profusion of data, high performance, tools and systems are needed to align the sequences from the base reads and extract the information hidden within.

Biological computation is highly intensive with a large computational load that grows exponentially with the dimension of the problem domain. General Processing Units (GPUs) have been employed to accelerate computations by enabling computations to be performed in parallel [4]. Ramdas and Egan (IEEE TENCON 2005) in highlighted that “Software implementation of some sequence alignment algorithms suffer quadratic time performance” while highly parallelised solutions “provide linear time performance”. FPGA hardware offers an enhanced speed up for parallel processing and have been used to enhance the genomics computational pipeline.

This PhD will investigate the use of FPGA Hardware for parallelising computation, development of new architectures for algorithm acceleration, and other key mechanisms such as networks-on-chip interconnect strategies, to enhance the computation throughput for HTS. Performance enhancement is critical to support HTS in personalised medicine and therefore compact/efficient FPGA designs are sought to provide desktop high-performance computing (HPC).

This project is a collaboration between Dr Harkin and Lightbody on FPGA Hardware Acceleration, and Dr Shukla on bioinformatics and clinical decision making tools/tests for personalised/stratified medicine of degenerative diseases. Dr Shukla  will guide the PhD student on the computational bottlenecks within the subject domain, and will provide intricate knowledge of algorithms and bioinformatics pipelines and Dr Lightbody and Dr Harkin will advise on scalable computation and interconnect implementation strategies for FPGA acceleration. External advice from Dr Blayney will provide the medical/biological expertise and knowledge on the project area with a focus on the analysis of omics data and clinico-pathological data.

The project has access to required resources including open-access data, modern FPGA design tools, hardware platforms and test equipment. The goals for the PhD student are:

1).  Develop deep knowledge of the problem domain – with a focus on sequence alignment and computational bottlenecks.

2).  Understand the state-of-the-art in HPC solutions for computational biology and determine which bottleneck(s) could be resolved through parallelism.

3).  Investigate tools and hardware solutions for FPGAs, note with advances in acceleration stack sets this may include a combination of CPU, GPU and FPGA processing.

4).  Investigate novel architectures (scalable) for these solutions and implement chosen architecture(s), optimising for performance and providing comparative metrics against relevant prior work.

Potential impact from the PhD resides in reducing the computational cost for processing HTS data, which eventually will help to push personalised medicine more into clinical practice.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living