Genome Biology (Feb 2024)

RUBICON: a framework for designing efficient deep learning-based genomic basecallers

  • Gagandeep Singh,
  • Mohammed Alser,
  • Kristof Denolf,
  • Can Firtina,
  • Alireza Khodamoradi,
  • Meryem Banu Cavlak,
  • Henk Corporaal,
  • Onur Mutlu

DOI
https://doi.org/10.1186/s13059-024-03181-2
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 29

Abstract

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Abstract Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

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