RUBICON: a framework for designing efficient deep learning-based genomic basecallers
Gagandeep Singh, Mohammed Alser, Kristof Denolf, Can Fırtına, Alireza Khodamoradi, Meryem Banu Cavlak, Henk Corporaal, Onur Mutlu
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.