FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis.

Yilin Feng, Zheyu Li, Gulsum Gudukbay Akbulut, Vijaykrishnan Narayanan, Mahmut Taylan Kandemir, Chita R Das

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Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yilin Feng *Department of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA. ypf5071@psu.edu.

Zheyu Li *Department of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Gulsum Gudukbay AkbulutDepartment of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Vijaykrishnan NarayananDepartment of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Mahmut Taylan KandemirDepartment of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Chita R DasDepartment of Computer Science and Engineering, The Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Funding

DOE Grant DE-SC0023186NSF Grant 1931531

6 · The paper itself

Abstract

backgroundAdaptive Banded Event Alignment (ABEA) stands as a critical algorithmic component in sequence polishing and DNA methylation detection, employing dynamic programming to align raw Nanopore signal with reference reads. Motivated by the observation that, compared to CPUs and GPUs, cutting-edge FPGAs demonstrate-in certain cases-superior performance at a reduced cost and energy consumption, this paper presents an efficient FPGA-based accelerator for ABEA, leveraging the inherent high parallelism and sequential access pattern within ABEA.

resultOur proposed FPGA-based ABEA accelerator significantly enhances ABEA performance compared to the original CPU-based implementation in Nanopolish as well as the state-of-art acceleration on GPU and FPGA platforms. Specifically, targeting Xilinx VU9P, our accelerator achieves an average throughput speedup of 10.05

conclusionOur work demonstrates that intensive genome analysis can benefit significantly from cutting-edge FPGAs, offering improvements in both performance and energy consumption.

Indexed as

Nanopore SequencingSequence AlignmentSequence Analysis, DNAAlgorithmsFPGAsGenome polishingHardware accelerationNanopore sequencingSignal-level alignment

Identifiers

PMID40097942

DOI10.1186/s12859-024-06011-1

PMCPMC11917103

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LicenceCC BY-NC-ND

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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.