Mohammed Alser1,2,3, Hasan Hassan1, Akash Kumar2, Onur Mutlu1,3, Can Alkan3. 1. Computer Science Department, ETH Zürich, Zürich, Switzerland. 2. Chair for Processor Design, Center For Advancing Electronics Dresden, Institute of Computer Engineering, Technische Universität Dresden, Dresden, Germany. 3. Computer Engineering Department, Bilkent University, Ankara, Turkey.
Abstract
MOTIVATION: The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm. RESULTS: Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMU-SAFARI/Shouji. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm. RESULTS: Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMU-SAFARI/Shouji. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Kevin Judd McKernan; Heather E Peckham; Gina L Costa; Stephen F McLaughlin; Yutao Fu; Eric F Tsung; Christopher R Clouser; Cisyla Duncan; Jeffrey K Ichikawa; Clarence C Lee; Zheng Zhang; Swati S Ranade; Eileen T Dimalanta; Fiona C Hyland; Tanya D Sokolsky; Lei Zhang; Andrew Sheridan; Haoning Fu; Cynthia L Hendrickson; Bin Li; Lev Kotler; Jeremy R Stuart; Joel A Malek; Jonathan M Manning; Alena A Antipova; Damon S Perez; Michael P Moore; Kathleen C Hayashibara; Michael R Lyons; Robert E Beaudoin; Brittany E Coleman; Michael W Laptewicz; Adam E Sannicandro; Michael D Rhodes; Rajesh K Gottimukkala; Shan Yang; Vineet Bafna; Ali Bashir; Andrew MacBride; Can Alkan; Jeffrey M Kidd; Evan E Eichler; Martin G Reese; Francisco M De La Vega; Alan P Blanchard Journal: Genome Res Date: 2009-06-22 Impact factor: 9.043
Authors: Goncalo R Abecasis; Adam Auton; Lisa D Brooks; Mark A DePristo; Richard M Durbin; Robert E Handsaker; Hyun Min Kang; Gabor T Marth; Gil A McVean Journal: Nature Date: 2012-11-01 Impact factor: 49.962
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