A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing

This paper presents the first end-to-end next-generation sequencing (NGS) data analysis accelerator for short-read mapping, haplotype calling, variant calling, and genotyping. It supports both single-end and paired-end short-reads (or reads) and uses the FM-index, a compact index data structure, for exact-match in short-read mapping. For inexact match part of short-read mapping, a dynamic programming array is proposed to determine the mapping results. To reduce the workload of short-read mapping, a rapid similarity calculation is designed. A rescue technique is also adopted to increase the overall sensitivity. In haplotype calling, a parallel $k$-mer processing engine can construct the de Bruijn graph and assemble the haplotypes. The variant calling step determines variants between a subject and a reference genome sequence with a variant discovery engine. Lastly, genotype likelihood is computed in parallel by a genotype likelihood computing engine, which outputs genotypes of all discovered variants and corresponding Phred-scaled likelihood (PL) values. This work completes end-to-end data analysis for the 50$\boldsymbol{\times}$ PrecisionFDA dataset in an average of 28.2 minutes. It achieves a 3-to-59$\boldsymbol{\times}$ higher throughput than the existing solutions with higher precision (99.79%) and sensitivity (99.03%). The chip also achieves a 935$\boldsymbol{\times}$ higher energy efficiency than the Illumina DRAGEN FPGA acceleration system.

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Publication Details

Journal
IEEE Transactions on Biomedical Circuits and Systems
Published
2025-03-27
DOI
https://doi.org/10.1109/tbcas.2025.3555579
Citations
1
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.94
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article

A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing

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1 citations
IEEE Transactions on Biomedical Circuits and Systems
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article

A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing

Yen‐Lung Chen, Yi-Chung Wu, Nian-Shyang Chang, Jui‐Hung Hung, Wen-Ching Chen, Liang-Yi Lin, Chao-Hsi Lee, Chi‐Shi Chen, Chung-Hsuan Yang, Chun‐Pin Lin, Chia‐Hsiang Yang
article en
1 citations

Abstract

This paper presents the first end-to-end next-generation sequencing (NGS) data analysis accelerator for short-read mapping, haplotype calling, variant calling, and genotyping. It supports both single-end and paired-end short-reads (or reads) and uses the FM-index, a compact index data structure, for exact-match in short-read mapping. For inexact match part of short-read mapping, a dynamic programming array is proposed to determine the mapping results. To reduce the workload of short-read mapping, a rapid similarity calculation is designed. A rescue technique is also adopted to increase the overall sensitivity. In haplotype calling, a parallel $k$-mer processing engine can construct the de Bruijn graph and assemble the haplotypes. The variant calling step determines variants between a subject and a reference genome sequence with a variant discovery engine. Lastly, genotype likelihood is computed in parallel by a genotype likelihood computing engine, which outputs genotypes of all discovered variants and corresponding Phred-scaled likelihood (PL) values. This work completes end-to-end data analysis for the 50$\boldsymbol{\times}$ PrecisionFDA dataset in an average of 28.2 minutes. It achieves a 3-to-59$\boldsymbol{\times}$ higher throughput than the existing solutions with higher precision (99.79%) and sensitivity (99.03%). The chip also achieves a 935$\boldsymbol{\times}$ higher energy efficiency than the Illumina DRAGEN FPGA acceleration system.

IEEE Transactions on Biomedical Circuits and SystemsVol. 19(6)
National Yang Ming Chiao Tung University (TW), National Taiwan University (TW), Taiwan Semiconductor Manufacturing Company (Taiwan) (TW)
Openalex Percentile: Top 27%
Cancer Genomics and Diagnostics
0.94
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