A deep learning model for predicting next-generation sequencing depth from DNA sequence

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dc.citation.articleNumber4387en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorZhang, Jinny X.en_US
dc.contributor.authorYordanov, Boyanen_US
dc.contributor.authorGaunt, Alexanderen_US
dc.contributor.authorWang, Michael X.en_US
dc.contributor.authorDai, Pengen_US
dc.contributor.authorChen, Yuan-Jyueen_US
dc.contributor.authorZhang, Kerouen_US
dc.contributor.authorFang, John Z.en_US
dc.contributor.authorDalchau, Neilen_US
dc.contributor.authorLi, Jiamingen_US
dc.contributor.authorPhillips, Andrewen_US
dc.contributor.authorZhang, David Yuen_US
dc.contributor.orgSystems, Synthetic, and Physical Biologyen_US
dc.date.accessioned2021-08-05T15:51:55Zen_US
dc.date.available2021-08-05T15:51:55Zen_US
dc.date.issued2021en_US
dc.description.abstractTargeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.en_US
dc.identifier.citationZhang, Jinny X., Yordanov, Boyan, Gaunt, Alexander, et al.. "A deep learning model for predicting next-generation sequencing depth from DNA sequence." <i>Nature Communications,</i> 12, (2021) Springer Nature: https://doi.org/10.1038/s41467-021-24497-8.en_US
dc.identifier.digitals41467-021-24497-8en_US
dc.identifier.doihttps://doi.org/10.1038/s41467-021-24497-8en_US
dc.identifier.urihttps://hdl.handle.net/1911/111139en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleA deep learning model for predicting next-generation sequencing depth from DNA sequenceen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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