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SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs
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TLDR
SneakySnake is introduced, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment and is efficient to implement on CPUs, GPUs, and FPGAs.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 37.7x and 43.9x (>12x on average), respectively, with its CPU implementation, and by up to 413x and 689x (>400x 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 979x (276.9x on average) and 91.7x (31.7x 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: this https URLread more
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Proceedings ArticleDOI
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
Damla Senol Cali,Kalsi Gurpreet S,Zülal Bingöl,Can Firtina,Lavanya Subramanian,Jeremie S. Kim,Rachata Ausavarungnirun,Mohammed Alser,Juan Gómez-Luna,Amirali Boroumand,Anant Norion,Allison Scibisz,Sreenivas Subramoneyon,Can Alkan,Saugata Ghose,Onur Mutlu +15 more
TL;DR: GenASM as discussed by the authors accelerates read alignment for both long reads and short reads, with 3.7× the performance of a state-of-the-art pre-alignment filter.
Proceedings ArticleDOI
GenStore: a high-performance in-storage processing system for genome sequence analysis
Nika Mansouri Ghiasi,Ji-Soo Park,Harun Mustafa,Jeremie S. Kim,Ataberk Olgun,Arvid Gollwitzer,Damla Senol Cali,Can Fırtına,Haiyu Mao,N. Alserr,Rachata Ausavarungnirun,Nandita Vijaykumar,Mohammed Alser,Onur Mutlu +13 more
TL;DR: GenStore is proposed, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequenceAnalysis by exploiting low-cost and accurate in- storage filters.
Proceedings ArticleDOI
SeGraM: a universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping
Damla Senol Cali,Konstantinos Kanellopoulos,Joel Lindegger,Zülal Bingöl,Gurpreet Singh Kalsi,Ziyi Zuo,Can Fırtına,Meryem Banu Cavlak,Jeremie S. Kim,Nika Mansouri Ghiasi,Gagandeep Singh,Juan G'omez-Luna,N. Alserr,Mohammed Alser,Sreenivas Subramoney,Can Alkan,Saugata Ghose,Onur Mutlu +17 more
TL;DR: This work proposes SeGraM, a universal algorithm/hardware co-designed genomic mapping accelerator that can effectively and efficiently support both sequence-to-graph mapping and sequence- to-sequence mapping, for both short and long reads.
Journal Article
GenStore: A High-Performance and Energy-Efficient In-Storage Computing System for Genome Sequence Analysis
Nika Mansouri Ghiasi,Ji-Soo Park,Harun Mustafa,Jeremie S. Kim,Ataberk Olgun,Arvid Gollwitzer,Damla Senol Cali,Can Fırtına,Haiyu Mao,N. Alserr,Rachata Ausavarungnirun,Nandita Vijaykumar,Mohammed Alser,Onur Mutlu +13 more
TL;DR: Through rigorous analysis of read mapping processes of reads with different properties and degrees of genetic variation, this work meticulously design low-cost hardware accelerators and data/computation flows inside a NAND flashbased solid-state drive (SSD).
Journal ArticleDOI
From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures
Mohammed Alser,Joel Lindegger,Can Fırtına,Nour Almadhoun,Haiyu Mao,Gagandeep Singh,Juan Gómez-Luna,Onur Mutlu +7 more
TL;DR: In this article , the authors describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures, and conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics.
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A general method applicable to the search for similarities in the amino acid sequence of two proteins
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A Formal Basis for the Heuristic Determination of Minimum Cost Paths
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