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bioRxiv · 10.1101/2022.11.20.517297

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

Singh, G.·Alser, M.·Khodamoradi, A.·Denolf, K.·Firtina, C.·Cavlak, M. B.·Corporaal, H.·Mutlu, O.
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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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Singh, G., Alser, M., Khodamoradi, A., Denolf, K., Firtina, C., Cavlak, M. B., Corporaal, H., Mutlu, O.. 2022-11-22. A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers. https://doi.org/10.1101/2022.11.20.517297

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