<p>RUBICON: A framework for designing efficient deep learning-based genomic basecallers</p>

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

doi:10.7490/f1000research.1119269.1

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Singh G, Alser M, Khodamoradi A et al. RUBICON: A framework for designing efficient deep learning-based genomic basecallers [version 1; not peer reviewed]. F1000Research 2022, 11:1436 (poster) (https://doi.org/10.7490/f1000research.1119269.1)

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

Gagandeep Singh¹, Mohammed Alser, Alireza Khodamoradi, Kristof Denolf, Can Firtina, Meryem Banu Cavlak, Henk Corporaal, Onur Mutlu¹

Published 05 Dec 2022 (https://doi.org/10.7490/f1000research.1119269.1)

Author Affiliations

¹ ETH Zürich, Switzerland

Copyright © 2022 Singh G et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 05 Dec 2022

RUBICON: A framework for designing efficient deep learning-based genomic basecallers
[version 1; not peer reviewed]

Gagandeep Singh¹, Mohammed Alser, Alireza Khodamoradi, Kristof Denolf, Can Firtina, Meryem Banu Cavlak, Henk Corporaal, Onur Mutlu¹

Author Affiliations

¹ ETH Zürich, Switzerland

Presented at

Pacific Symposium on Biocomputing (PSB), 2023

Competing Interests

No competing interests were disclosed

Keywords

Genome sequencing, basecalling, machine learning, neural networks, hardware acceleration

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