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A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
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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.
Title: A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Description:
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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