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Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.
Lopez-Rincon, Alejandro; Tonda, Alberto; Mendoza-Maldonado, Lucero; Mulders, Daphne G J C; Molenkamp, Richard; Perez-Romero, Carmina A; Claassen, Eric; Garssen, Johan; Kraneveld, Aletta D.
  • Lopez-Rincon A; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, The Netherlands. a.lopezrincon@uu.nl.
  • Tonda A; UMR 518 MIA-Paris, INRAE, c/o 113 rue Nationale, 75103, Paris, France.
  • Mendoza-Maldonado L; Hospital Civil de Guadalajara "Dr. Juan I. Menchaca", Salvador Quevedo y Zubieta 750, Independencia Oriente, C.P. 44340, Guadalajara, Jalisco, México.
  • Mulders DGJC; Department of Viroscience, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Molenkamp R; Department of Viroscience, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Perez-Romero CA; Departamento de Investigación, Universidad Central de Queretaro (UNICEQ), Av. 5 de Febrero 1602, San Pablo, 76130, Santiago de Querétaro, QRO, Mexico.
  • Claassen E; Athena Institute, Vrije Universiteit, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.
  • Garssen J; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, The Netherlands.
  • Kraneveld AD; Department Immunology, Danone Nutricia research, Uppsalalaan 12, 3584 CT, Utrecht, The Netherlands.
Sci Rep ; 11(1): 947, 2021 01 13.
Article in English | MEDLINE | ID: covidwho-1065932
ABSTRACT
In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Polymerase Chain Reaction / DNA Primers / Limit of Detection / Deep Learning / SARS-CoV-2 Type of study: Diagnostic study / Prognostic study Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-020-80363-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Polymerase Chain Reaction / DNA Primers / Limit of Detection / Deep Learning / SARS-CoV-2 Type of study: Diagnostic study / Prognostic study Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-020-80363-5