ArticleBioinformatics (Oxford, England)2019
Shouji: a fast and efficient pre-alignment filter for sequence alignment.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 82 citations in OpenAlex.
- A survey of sequence-to-graph mapping algorithms in the pangenome era.Genome biology · 2025Review
- FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis.BMC bioinformatics · 2025Article
- QuickEd: high-performance exact sequence alignment based on bound-and-align.Bioinformatics (Oxford, England) · 2025Article
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- WFA-GPU: gap-affine pairwise read-alignment using GPUs.Bioinformatics (Oxford, England) · 2023Article
- A framework for high-throughput sequence alignment using real processing-in-memory systems.Bioinformatics (Oxford, England) · 2023Article
- Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs.Bioinformatics (Oxford, England) · 2023Article
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.Computational and structural biotechnology journal · 2022Review
- Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps.PloS one · 2022Article
- Technology dictates algorithms: recent developments in read alignment.Genome biology · 2021Review
- Hardware acceleration of genomics data analysis: challenges and opportunities.Bioinformatics (Oxford, England) · 2021Article
- Accel-Align: a fast sequence mapper and aligner based on the seed-embed-extend method.BMC bioinformatics · 2021Article
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe 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.
resultsShouji 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.