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A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

Gagandeep Singh, Mohammed Alser, Alireza Khodamoradi, Kristof Denolf, Can Firtina, Meryem Banu Cavlak, Henk Corporaal, Onur Mutlu
doi: https://doi.org/10.1101/2022.11.20.517297

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doi 
https://doi.org/10.1101/2022.11.20.517297
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  • December 8, 2022.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Gagandeep Singha,†,
  2. Mohammed Alser*,a,
  3. Alireza Khodamoradi*,b,
  4. Kristof Denolfb,
  5. Can Firtinaa,
  6. Meryem Banu Cavlaka,
  7. Henk Corporaalc and
  8. Onur Mutlua
  1. aETH Zürich
  2. bAMD
  3. cEindhoven University of Technology
  1. ↵†Corresponding author; email: gagan.posted{at}gmail.com

  1. ↵* These authors contributed equally to this work.

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Posted December 08, 2022.
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A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Gagandeep Singh, Mohammed Alser, Alireza Khodamoradi, Kristof Denolf, Can Firtina, Meryem Banu Cavlak, Henk Corporaal, Onur Mutlu
bioRxiv 2022.11.20.517297; doi: https://doi.org/10.1101/2022.11.20.517297
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