Claudia Barrera Garzon‡, Karin Pritsch§‡, Fabian Weikl‡Corresponding author: Claudia Barrera Garzon ( claudia.barrera@tum.de ) © Claudia Barrera Garzon, Karin Pritsch, Fabian Weikl. This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
Barrera Garzon C, Pritsch K, Weikl F (2026) Combined strategies to evaluate fungal rhizosphere communities using Nanopore sequencing. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e201349 |  |
Evaluating the diversity of soil fungal communities is critical to understanding their role in plant adaptation and ecology. Improving the resolution of fungal identification using new sequencing techniques, such as Oxford Nanopore, which yield long read lengths, is therefore highly desirable. Yet, the implementation of this sequencing technology for fungi and other eukaryotes has just begun.
In this study, we evaluated different primer combinations that cover the entire internal transcribed spacer (ITS) region and include fragments of the small (SSU) and large subunit (LSU) regions. We designed a pipeline to recover most of the fungal diversity, compared two classifiers and two databases, and added a peptide nucleic acid (PNA) to inhibit the co-amplification of plant ITS.
Our results showed that including the LSU did not improve the resolution of individual-strain classification, and that non-degenerate forward primers were more effective for ITS sequence extraction, recovering up to 91% of strains in a mock community with an accuracy of 0.97. Moreover, results with rhizosphere samples showed that adding PNA effectively removed host contamination across most primer combinations, thereby improving read retention during data processing. Additionally, host decontamination and ITS extraction steps enhanced the recovery of fungal groups, mainly in the Ascomycota phylum.
Altogether, our study provides strategies for handling samples with high plant tissue concentrations, from primer selection to fungal species classification, and presents a modular pipeline to facilitate data processing tailored to the user's needs.