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Calculating the similarities between a pair of genomic sequences is one ofthe most fundamental computational steps in genomic analysis. This step --called sequence alignment -- is the computational bottleneck because: (1) it isimplemented using quadratic-time dynamic programming algorithms and (2) themajority of candidate locations in the reference genome do not align with agiven read due to high dissimilarity. Calculating the alignment of suchincorrect candidate locations consumes an overwhelming majority of a modernread mapper's execution time.  In this thesis, we introduce four new algorithms (GateKeeper, Shouji, MAGNET,and SneakySnake) that function as a pre-alignment step and aim to filter outmost incorrect candidate locations. The first key idea of our pre-alignmentfilters is to provide high filtering accuracy by correctly detecting allsimilar segments shared between two sequences. The second key idea is toexploit the massively parallel architecture of modern FPGAs for acceleratingour filtering algorithms. We also develop an efficient CPU implementation ofthe SneakySnake algorithm for commodity desktops and servers. We evaluate thebenefits and downsides of our pre-alignment filtering approach in detail using12 real datasets. In our evaluation, we demonstrate that our hardwarepre-alignment filters show two to three orders of magnitude speedup over theirequivalent CPU implementations. We also demonstrate that integrating ourhardware pre-alignment filters with the state-of-the-art read aligners reducesthe aligner's execution time by up to 21.5x. Finally, we show that efficientCPU implementation of pre-alignment filtering still provides significantbenefits. We show that SneakySnake on average reduces the execution time of thebest performing CPU-based read aligners Edlib and Parasail, by up to 43x and57.9x, respectively.

Accelerating the Understanding of Life's Code Through Better Algorithms and Hardware Design