Untrimmed ITS2 metabarcode sequences cause artificially reduced abundances of specific fungal taxa

Advances in DNA metabarcoding have greatly expanded our knowledge of microbial communities in recent years. Pipelines and parameters have been tested extensively for bacterial metabarcoding using the 16S rRNA gene and best practices are largely established. For fungal metabarcoding using the ITS gene, however, only a few studies have considered how such pipelines and parameters can affect community prediction. Here we report a novel bias uncovered during ITS2 sequencing of Trichoderma -infected ant fungus gardens and confirmed using mock communities. Abnormally low forward read quality caused Trichoderma ITS2 reads to be computationally filtered before and during read pair merging, thus almost entirely eliminating Trichoderma ASVs from the resulting fungal community profiles. Sliding window quality trimming before filtering allowed most of these reads to pass filtering and merge successfully, producing community profiles that now correlated with visual signs of Trichoderma infection and matched the composition of the mock communities. Applying such sliding window trimming to a previously generated environmental ITS2 dataset increased the detected fungal diversity and again overcame read quality biases against Trichoderma to instead detect it in nearly every sample and often at high relative abundances. This analysis additionally identified a similar, but distinct, bias against a second fungal genus Meyerozyma . The prevalence of such quality biases against other fungal ITS sequences is unknown but may be widespread. We therefore advocate for routine use of sliding window quality trimming as a best practice in ITS2 metabarcoding analysis.