Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extended metabarcodes
Keyword(s):
The increased usage of long-read sequencing for metabarcoding has not been matched with public databases suited for error-prone long-reads. We address this gap and present a proof-of-concept study for classifying fungal species using linked machine learning classifiers. We demonstrate its capability for accurate classification using a labelled fungal sequencing dataset of 44 species. We show the advantage of our approach for closely related species over current alignment and k-mer methods and suggest a confidence threshold of 0.85 to maximise accurate target species identification from complex samples of unknown composition. We suggest future use of this approach in medicine, agriculture, and biosecurity.
2019 ◽
Vol 7
(8)
◽
pp. 122-129
2020 ◽
Vol 51
(2)
◽
pp. 329-335
Keyword(s):
2021 ◽
Vol 2021
(1)
◽
2020 ◽
Vol 928
◽
pp. 032027
Keyword(s):