Fast Processing of Environmental DNA Metabarcoding Sequence Data Using Convolutional Neural Networks
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.22.445213; this version posted May 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fast processing of environmental DNA metabarcoding sequence data using convolutional neural networks Benjamin Fl¨uck 1,2*, La¨etitiaMathon 3, St´ephanieManel 3, Alice Valentini 4, Tony Dejean 4, Camille Albouy5, David Mouillot6,7, Wilfried Thuiller8, J´er^omeMurienne 9, S´ebastienBrosse 9, and Lo¨ıcPellissier 1,2* 1Department of Environmental System Science, ETH Z¨urich, 8092 Z¨urich, Switzerland 2Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland 3CEFE, Univ. Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France 4SPYGEN, Le Bourget-du-Lac, France 5IFREMER, unit´e Ecologie´ et Mod`elespour l'Halieutique, rue de l'Ile d'Yeu, BP21105, 44311 Nantes cedex 3, France 6MARBEC, Univ. Montpellier,CNRS, IRD, Ifremer, Montpellier, France 7Institut Universitaire de France, IUF, Paris 75231, France 8Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d'Ecologie´ Alpine F- 38000 Grenoble, France 9Laboratoire Evolution et Diversit´eBiologique (UMR5174), CNRS, IRD, Universit´ePaul Sabatier, Toulouse, France *Corresponding authors: [benjamin.flueck, loic.pellissier]@usys.ethz.ch May 22, 2021 1 Abstract quences per minute which was 150 times faster than with OBITools. Once trained, the application of CNN The intensification of anthropogenic pressures have in- to new eDNA metabarcoding data can be automated, creased consequences on biodiversity and ultimately which promises fast and easy deployment on the cloud on the functioning of ecosystems.
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