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GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis

View ORCID ProfileHasindu Gamaarachchi, Chun Wai Lam, Gihan Jayatilaka, Hiruna Samarakoon, Jared T. Simpson, View ORCID ProfileMartin A. Smith, Sri Parameswaran
doi: https://doi.org/10.1101/756122
Hasindu Gamaarachchi
1School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
2Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia
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  • For correspondence: hasindu@unsw.edu.au
Chun Wai Lam
1School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
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Gihan Jayatilaka
3Department of Computer Engineering, University of Peradeniya, Peradeniya, Sri Lanka
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Hiruna Samarakoon
3Department of Computer Engineering, University of Peradeniya, Peradeniya, Sri Lanka
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Jared T. Simpson
5Ontario Institute for Cancer Research, Toronto, Canada
6Department of Computer Science, University of Toronto, Toronto, Canada
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Martin A. Smith
2Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia
4St-Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia
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Sri Parameswaran
1School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
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Abstract

Nanopore sequencing has the potential to revolutionise genomics by realising portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these applications requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. For instance, comparing raw nanopore signals to a biological reference sequence is a computationally complex task despite leveraging a dynamic programming algorithm for Adaptive Banded Event Alignment (ABEA)—a commonly used approach to polish sequencing data and identify non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. By optimising memory, compute and load balancing between CPU and GPU, we demonstrate how f5c can perform ~3-5× faster than the original implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.

Footnotes

  • https://github.com/hasindu2008/f5c/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 05, 2019.
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GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis
Hasindu Gamaarachchi, Chun Wai Lam, Gihan Jayatilaka, Hiruna Samarakoon, Jared T. Simpson, Martin A. Smith, Sri Parameswaran
bioRxiv 756122; doi: https://doi.org/10.1101/756122
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GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis
Hasindu Gamaarachchi, Chun Wai Lam, Gihan Jayatilaka, Hiruna Samarakoon, Jared T. Simpson, Martin A. Smith, Sri Parameswaran
bioRxiv 756122; doi: https://doi.org/10.1101/756122

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