FPGASW: Accelerating Large-Scale Smith–Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array

Xia, Fei; Zou, Dan; Liu, Lu; Man, Xin; Zhang, Chunlei

doi:10.1007/s12539-017-0225-8

Cited by 54 publications

(76 citation statements)

References 38 publications

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“…Shouji, MAGNET and GateKeeper (Alser et al, 2017a) still significantly reduce the overall execution time of both FPGA and GPU based aligners. Shouji reduces the overall alignment time of FPGASW (Fei et al, 2018), CUDASWþþ 3.0 (Liu et al, 2013) and GSWABE (Liu and Schmidt, 2015) by factors of up to 14.5, 14.2 and 17.9Â, respectively. This is up to 1.35, 1.4 and 85Â more than the effect of MAGNET, GateKeeper and SHD on the end-to-end alignment time.…”

Section: Effects Of Pre-alignment Filtering On Sequence Alignmentmentioning

confidence: 99%

Shouji: a fast and efficient pre-alignment filter for sequence alignment

et al. 2019

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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.

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Section: Effects Of Pre-alignment Filtering On Sequence Alignmentmentioning

confidence: 99%

Shouji: a fast and efficient pre-alignment filter for sequence alignment

et al. 2019

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“…Edit distance is the primary calculation metric used to quantitatively measure dissimilarity between two sequences ( Fei et al , 2018 ). Thus, it is fundamental within SRA and typically implemented via the Levenshtein or Hamming distance calculation ( Zokaee et al., 2018 ).…”

Section: Genome Sequencing and Genomic Data Analysismentioning

confidence: 99%

“…The high performance of GPUs, however, results in considerable power consumption compared to FPGAs ( Yano et al , 2014 ). Despite this, GPUs are popular within high-performance computing and particularly within bioinformatics due to the relative ease at which a designer may implement an already existing short read alignment algorithm such as BWA ( Fei et al , 2018 ; Houtgast et al , 2018 ).…”

Section: Computational Challenges Of Short Read Alignmentmentioning

confidence: 99%

Hardware acceleration of genomics data analysis: challenges and opportunities

2021

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The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

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“…For a subject sequence with length m and a query sequence with length n, the conventional one-piece scoring model requires three matrices H, I, and D, all sized m × n, to be filled using Eqs. (1) to (3) below. This process is usually referred as the dynamic-programming stage (DP stage) in sequence alignment.…”

Section: Introductionmentioning

confidence: 99%

A Memory-Efficient Accelerator for DNA Sequence Alignment with Two-Piece Affine Gap Tracebacks

Lin

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Previously, dynamic-programming-based DNA sequence aligners were mostly implemented with a penalty function of the one-piece affine gap model. When aligning sequences with longer gaps, the two-piece affine gap model provides better results at the cost of memory usage, which becomes an issue especially for aligners with memory-hungry traceback capabilities. In this paper, we design a memory-efficient scheme for traceback recording with the two-piece penalty scoring, so that the aligner can be realized on an ASIC. Our design is implemented with TSMC 40nm technology, and the proposed aligner can speed up pairwise alignment by 71× compared to the CPU approach.

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FPGASW: Accelerating Large-Scale Smith–Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array

Cited by 54 publications

References 38 publications

Shouji: a fast and efficient pre-alignment filter for sequence alignment

Shouji: a fast and efficient pre-alignment filter for sequence alignment

Hardware acceleration of genomics data analysis: challenges and opportunities

A Memory-Efficient Accelerator for DNA Sequence Alignment with Two-Piece Affine Gap Tracebacks

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