The field of Bioinformatics and Computational Biology (BCB) a relatively new discipline which spans the boundaries of Biology, Computer Science and Engineering, and aims to develop systems that help organise, store, retrieve and analyse genomic and other biological information in a convenient and speedy way. This new discipline emerged mainly as a result of the Human Genome project which succeeded in transcribing the complete DNA sequence of the human genome, hence making it possible to address many problems which were impossible to even contemplate before, with a plethora of applications including disease diagnosis, drug engineering, bio-material engineering and genetic engineering of plants and animals; all with a real impact on the quality of life of ordinary individuals.
Due to the sheer immensity of the data sets involved in BCB algorithms (often measured in tens/hundreds of Gigabytes) as well as their computation demands (often measured in Tera-Ops), high performance supercomputers and computer clusters have been used as implementation platforms for high performance BCB computing. However, the high cost as well as the lack of suitable programming interfaces for these platforms still impedes a wider undertaking of this technology in the BCB community. Moreover, with increased heat dissipation, supercomputers are now often augmented with special purpose hardware (or ASICs) in order to speed up their operations while reducing their power dissipation. However, since ASICs are fully customised to implement particular tasks/algorithms, they suffer from increased development times, higher Non-Recurring-Engineering (NRE) costs, and inflexibility as they cannot be reused to implement tasks/algorithms other than those they have been designed to perform.
In order to counter the aforementioned limitations of both supercomputers and ASICs, this research proposes the use of state-of-the-art reprogrammable system-on-chip technology, in the form of platform FPGAs, as a relatively low cost, high performance and reprogrammable implementation platform for BCB applications. This project aims to develop a sophisticated library of FPGA architectures for bio-sequence analysis, microarray data analysis, and molecular dynamics simulation.
Selected Publications
- K. Benkrid, Y Liu, A. Benkrid, ‘A Highly Parameterised and Efficient FPGA-Based Skeleton for Pairwise Biological Sequence Alignment’, IEEE Transactions on Very Large Scale Integration (VLSI Systems), Vol. 17, Issue 4, pp. 561-570, April 2009
- S. Kasap, K. Benkrid and Y. Liu, ‘Design and Implementation of an FPGA-based Core for Gapped BLAST Sequence Alignment with the Two-Hit Method’, IAENG Journal Engineering Letters, Special Issue on High Performance Reconfigurable Systems, Volume 16 Issue 3, pp. 443-452, 2008
Supervisor: Dr. Khaled Benkrid
