An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules

Lim, S.; Baek, I.; Bhatt, J.; Upadhyay, R.; Oh, S.; Cha, M.; Kim, M. S.; Meinhardt, L.; Ahn, E.

doi:10.1101/2025.11.01.686068

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bioRxiv · 10.1101/2025.11.01.686068

An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules

Lim, S.·Baek, I.·Bhatt, J.·Upadhyay, R.·Oh, S.·Cha, M.·Kim, M. S.·Meinhardt, L.·Ahn, E.

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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 frameworks 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.

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Lim, S., Baek, I., Bhatt, J., Upadhyay, R., Oh, S., Cha, M., Kim, M. S., Meinhardt, L., Ahn, E.. 2025-11-02. An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules. https://doi.org/10.1101/2025.11.01.686068

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