Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem.

ABSTRACT: High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinform

SUBMITTER: Fluck B 

PROVIDER: S-EPMC9205931 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature