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Title: FPGA acceleration of DNA sequencing analysis and storage
Author: Arram, James
ISNI:       0000 0004 6496 9929
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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In this work we explore how Field-Programmable Gate Arrays (FPGAs) can be used to alleviate the data processing bottlenecks in DNA sequencing. We focus our efforts on accelerating the FM-index, a data structure used to solve the computationally intensive string matching problems found in DNA sequencing analysis such as short read alignment. The main contributions of this work are: 1) We accelerate the FM-index using FPGAs and develop several novel methods for reducing the memory bottleneck of the search algorithm. These methods include customising the FM-index structure according to the memory architecture of the FPGA platform and minimising the number of memory accesses through both architectural and algorithmic optimisations. 2) We present a new approach for accelerating approximate string matching using the backtracking FM-index. This approach makes use of specialised approximate string matching modules and a run-time reconfigurable architecture in order to achieve both high sensitivity and high performance. 3) We extend the FM-index search algorithm for reference-based compression and accelerate it using FPGAs. This accelerated design is integrated into fastqZip and fastaZip, two new tools that we have developed for the fast and effective compression of sequence data stored in the FASTQ and FASTA formats respectively. We implement our designs on the Maxeler Max4 Platform and show that they are able to outperform state-of-the-art DNA sequencing analysis software. For instance, our hardware-accelerated compression tool for FASTQ data is able to achieve a higher compression ratio than the best performing tool, fastqz, whilst the average compression and decompression speeds are 25 and 43 times faster respectively.
Supervisor: Luk, Wayne Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral