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DNAscent v2: Detecting Replication Forks in Nanopore Sequencing Data with Deep Learning

View ORCID ProfileMichael A. Boemo
doi: https://doi.org/10.1101/2020.11.04.368225
Michael A. Boemo
1Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, United Kingdom
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  • For correspondence: mb915@cam.ac.uk
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Abstract

The detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method for the high-throughput measurement of DNA replication dynamics with single-molecule resolution. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-base resolution. DNAscent v2 comes equipped with an autoencoder that detects replication forks, origins, and termination sites in ONT sequencing reads from both synchronous and asynchronous cell populations, outcompeting previous versions and other tools across different experimental protocols. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/MBoemo/DNAscent

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 4.0 International license.
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Posted November 05, 2020.
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DNAscent v2: Detecting Replication Forks in Nanopore Sequencing Data with Deep Learning
Michael A. Boemo
bioRxiv 2020.11.04.368225; doi: https://doi.org/10.1101/2020.11.04.368225
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DNAscent v2: Detecting Replication Forks in Nanopore Sequencing Data with Deep Learning
Michael A. Boemo
bioRxiv 2020.11.04.368225; doi: https://doi.org/10.1101/2020.11.04.368225

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