Accelerating the understanding of life's code through better algorithms and hardware design

Our understanding of human genomes today is affected by the ability of modern computing technology to quickly and accurately determine an individual's entire genome. Over the past decade, high throughput sequencing (HTS) technologies have opened the door to remarkable biomedical discoveries through its ability to generate hundreds of millions to billions of DNA segments per run along with a substantial reduction in time and cost. However, this ood of sequencing data continues to overwhelm the processing capacity of existing algorithms and hardware. To analyze a patient's genome, each of these segments - called reads - must be mapped to a reference genome based on the similarity between a read and \candidate" locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (1) it is implemented using quadratic-time dynamic programming algorithms, and (2) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms. In this thesis, we introduce four new algorithms that function as a prealignment step and aim to filter out most incorrect candidate locations. We call our algorithms GateKeeper, Slider, MAGNET, and SneakySnake. The first key idea of our proposed pre-alignment filters is to provide high filtering accuracy by correctly detecting all similar segments shared between two sequences. The second key idea is to exploit the massively parallel architecture of modern FPGAs for accelerating our four proposed filtering algorithms. We also develop an efficient CPU implementation of the SneakySnake algorithm for commodity desktops and servers, which are largely available to bioinformaticians without the hassle of handling hardware complexity. We evaluate the benefits and downsides of our pre-alignment filtering approach in detail using 12 real datasets across different read length and edit distance thresholds. In our evaluation, we demonstrate that our hardware pre-alignment filters show two to three orders of magnitude speedup over their equivalent CPU implementations. We also demonstrate that integrating our hardware pre-alignment filters with the state-of-the-art read aligners reduces the aligner's execution time by up to 21.5x. Finally, we show that efficient CPU implementation of pre-alignment filtering still provides significant benefits. We show that SneakySnake on average reduces the execution time of the best performing CPU-based read aligners Edlib and Parasail, by up to 43x and 57.9x, respectively. The key conclusion of this thesis is that developing a fast and efficient filtering heuristic, and developing a better understanding of its accuracy together leads to significant reduction in read alignment's execution time, without sacrificing any of the aligner' capabilities. We hope and believe that our new architectures and algorithms catalyze their adoption in existing and future genome analysis pipelines.