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Improving long-read consensus sequencing accuracy with deep learning

View ORCID ProfileAvantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
doi: https://doi.org/10.1101/2021.06.28.450238
Avantika Lal
1NVIDIA Corporation, Santa Clara, CA 95051, USA
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Michael Brown
2Pacific Biosciences, Menlo Park, CA 94025, USA
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Rahul Mohan
1NVIDIA Corporation, Santa Clara, CA 95051, USA
3Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
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Joyjit Daw
1NVIDIA Corporation, Santa Clara, CA 95051, USA
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James Drake
2Pacific Biosciences, Menlo Park, CA 94025, USA
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Johnny Israeli
1NVIDIA Corporation, Santa Clara, CA 95051, USA
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  • For correspondence: jisraeli@nvidia.com
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Article Information

doi 
https://doi.org/10.1101/2021.06.28.450238
History 
  • June 30, 2021.

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  • You are currently viewing Version 1 of this article (June 30, 2021 - 14:55).
  • Version 2 (July 26, 2021 - 22:23).
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Copyright 
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-ND 4.0 International license.

Author Information

  1. Avantika Lal1,
  2. Michael Brown2,
  3. Rahul Mohan1,3,
  4. Joyjit Daw1,
  5. James Drake2 and
  6. Johnny Israeli1,*
  1. 1NVIDIA Corporation, Santa Clara, CA 95051, USA
  2. 2Pacific Biosciences, Menlo Park, CA 94025, USA
  3. 3Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
  1. ↵*Corresponding author; email: jisraeli{at}nvidia.com
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Posted June 30, 2021.
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Improving long-read consensus sequencing accuracy with deep learning
Avantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
bioRxiv 2021.06.28.450238; doi: https://doi.org/10.1101/2021.06.28.450238
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Improving long-read consensus sequencing accuracy with deep learning
Avantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
bioRxiv 2021.06.28.450238; doi: https://doi.org/10.1101/2021.06.28.450238

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