Dnabarcoder: An open-source software package for analysing and predicting DNA sequence similarity cutoffs for fungal sequence identification.

Abstract

The accuracy and precision of fungal molecular identification and classification are challenging, particularly in environmental metabarcoding approaches as these often trade accuracy for efficiency given the large data volumes at hand. In most ecological studies, only a single similarity cutoff value is used for sequence identification. This is not sufficient since the most commonly used DNA markers are known to vary widely in terms of inter‐ and intraspecific variability. We address this problem by presenting a new tool, dnabarcoder, to predict local similarity cutoffs and measure the resolving powers of a biomarker for sequence identification for different clades of fungi. It was shown that the predicted similarity cutoffs varied significantly between the clades of a recently released ITS DNA barcode data set from the CBS culture collection of the Westerdijk Fungal Biodiversity Institute. When classifying a large public fungal ITS data set—the UNITE database—against the barcode data set, the local similarity cutoffs assigned fewer sequences than the traditional cutoffs used in metabarcoding studies. However, the obtained accuracy and precision were significantly improved. Our study showed that it might be better to extract the ITS region from the ITS barcodes to optimize taxonomic assignment accuracy. Furthermore, 15.3, 25.6, and 26.3% of the fungal species of the barcode data set were indistinguishable by full‐length ITS, ITS1, and ITS2, respectively. Except for these indistinguishable species, the resolving powers of full‐length ITS, ITS1, and ITS2 sequences were similar at the species level. Nevertheless, the complete ITS region had a better resolving power at higher taxonomic levels.