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

Gagandeep Singh

doi:10.1186/s13059-024-03181-2

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

2024-02-16

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DOI：10.1186/s13059-024-03181-2

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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.

Keywords:

Genomics sequencing

Basecalling

Hardware acceleration

Machine learning

Deep neural network

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

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