SquiggleNet: real-time, direct classification of nanopore signals
dc.contributor.author | Bao, Yuwei | |
dc.contributor.author | Wadden, Jack | |
dc.contributor.author | Erb-Downward, John R. | |
dc.contributor.author | Ranjan, Piyush | |
dc.contributor.author | Zhou, Weichen | |
dc.contributor.author | McDonald, Torrin L. | |
dc.contributor.author | Mills, Ryan E. | |
dc.contributor.author | Boyle, Alan P. | |
dc.contributor.author | Dickson, Robert P. | |
dc.contributor.author | Blaauw, David | |
dc.contributor.author | Welch, Joshua D. | |
dc.date.accessioned | 2022-08-10T18:37:30Z | |
dc.date.available | 2022-08-10T18:37:30Z | |
dc.date.issued | 2021-10-27 | |
dc.identifier.citation | Genome Biology. 2021 Oct 27;22(1):298 | |
dc.identifier.uri | https://doi.org/10.1186/s13059-021-02511-y | |
dc.identifier.uri | https://hdl.handle.net/2027.42/173871 | en |
dc.description.abstract | Abstract We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements. | |
dc.title | SquiggleNet: real-time, direct classification of nanopore signals | |
dc.type | Journal Article | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/173871/1/13059_2021_Article_2511.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/5602 | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s) | |
dc.date.updated | 2022-08-10T18:37:29Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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