Mohammed Alser1,2, Taha Shahroodi1, Juan Gómez-Luna1,2, Can Alkan3, Onur Mutlu1,2,4,3. 1. Department of Computer Science, ETH Zurich, Zurich, Switzerland. 2. Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland. 3. Department of Computer Engineering, Bilkent University, Ankara, Turkey. 4. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Abstract
MOTIVATION: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs. RESULTS: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7× and 43.9 × (>12× on average), respectively, with its CPU implementation, and by up to 413× and 689 × (>400× on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979 × (276.9× on average) and 91.7 × (31.7× on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g., configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities. AVAILABILITY: https://github.com/CMU-SAFARI/SneakySnake. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
MOTIVATION: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs. RESULTS: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7× and 43.9 × (>12× on average), respectively, with its CPU implementation, and by up to 413× and 689 × (>400× on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979 × (276.9× on average) and 91.7 × (31.7× on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g., configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities. AVAILABILITY: https://github.com/CMU-SAFARI/SneakySnake. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
Authors: Mohammed Alser; Jeremy Rotman; Onur Mutlu; Serghei Mangul; Dhrithi Deshpande; Kodi Taraszka; Huwenbo Shi; Pelin Icer Baykal; Harry Taegyun Yang; Victor Xue; Sergey Knyazev; Benjamin D Singer; Brunilda Balliu; David Koslicki; Pavel Skums; Alex Zelikovsky; Can Alkan Journal: Genome Biol Date: 2021-08-26 Impact factor: 13.583