Authors
Lim, S., Baek, I., Bhatt, J., Upadhyay, R., Oh, S., Cha, M., Kim, M. S., Meinhardt, L., Ahn, E.
Abstract
Accurate fungal ITS diagnostics rely on public sequence archives, but heterogeneous record lengths, especially frequent truncation before the LSU/28S segment, cause naive in silico benchmarking to conflate primer performance with database incompleteness. To resolve this persistent "denominator error," we present an open and fully reproducible "eligibility-aware" framework. Our pipeline first establishes eligibility by confirming both primer sites are present before applying bench-realistic performance rules, including a strict penalty for 3'-terminal mismatches. It further provides mechanistic insights by analyzing binding-site conservation and uses a rarefaction-based approach to guide efficient quality control as databases grow. We demonstrate the framework's utility using the cacao pathosystem, a context where rapid differentiation of the fungal pathogen Moniliophthora from symptomatically similar oomycetes is critical. The result is a robust, field-ready diagnostic decision tree operable under a single touchdown (TD) PCR program. By providing a transparent and barcode-agnostic template, our eligibility-aware approach offers a significant methodological advance for designing and validating molecular assays in mycology and beyond.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 04 Nov 2025.
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