Biology subjects

Graetz, A.

Publications and source records attributed to Graetz, A..

3 recordsLinked to original sources

bioRxiv2025

Benchmarking fungal species classification using Oxford Nanopore Technologies long-read ITS metabarcodes

The gold standard for fungal classification has long been specimen or culture-based, however, many Fungi display simple morphological characteristics, or are unculturable by current methods. Using DNA metabarcodes, many taxa can be identified from a single sample simultaneously, but annotating these sequences with species-level taxonomy often requires more information than short-read sequences afford. Oxford Nanopore Technologies (ONT) long-read sequencing has achieved species-level resolution in metabarcoding of bacteria and invertebrates, but complex fungal taxonomy and biology has been a hurdle to the application of this technology for Fungi. In this work, we use a mock community of real ONT long-read metabarcodes from 54 fungi from the Dikarya subkingdom to extensively benchmark classification approaches to assign species-level taxonomy. We compare eight classification approaches spanning alignment and k-mer based algorithms, to emerging machine learning methods, and assess the sensitivity, precision, and diversity estimation of each classifier at the species level. Our results indicate that classifiers which determine informed thresholds of sequence similarity based on a provided reference database are not only more accurate at the species level, but more consistent to correct species abundance distributions, and better able to place sequences from unknown species taxonomically closer to their true origin. We demonstrate the power of machine learning classifiers to leverage long-read metabarcodes, and their promise as emerging methods in DNA sequence classification. We present our results in the context of real-world use cases, to demonstrate that species-level taxonomic inference is achievable, precise, and reliable with ONT long-read fungal metabarcodes.

molecular biology↗

bioRxiv2024

Mating-compatibility genes employed as diagnostic markers to identify novel incursions of the myrtle rust pathogen Austropuccinia psidii

Austropuccinia psidii is the causal agent of myrtle rust in over 480 species within the family Myrtaceae. Lineages of A. psidii are structured by host in its native range, and some have success on new-encounter hosts. For example, the pandemic biotype has spread beyond South America, and proliferation of other lineages is an additional risk to biodiversity and industries. Efforts to manage A. psidii incursions, including lineage differentiation, relies on variable microsatellite markers. Testing these markers is time-consuming and complex, particularly on a large scale. We designed a novel diagnostic approach targeting the fungal mating-type HD (homeodomain) transcription factor locus to address these limitations. The HD locus (bW1/2-HD1 and bE1/2-HD2) is highly polymorphic, facilitating clear biological predictions about its inheritance from founding populations. To be considered the same lineage, all four HD alleles must be identical. Our lineage diagnostics relies on PCR amplification of the HD locus in different genotypes of A. psidii followed by amplicon sequencing using Oxford Nanopore Technologies (ONT) and comparative analysis. The lineage-specific assay was validated on four isolates with existing genomes, uncharacterized isolates, and directly from infected leaf material. We reconstructed HD alleles from amplicons and confirmed their sequence identity relative to their reference. Genealogies using HD alleles confirmed the variations at the HD loci among lineages/isolates. Our study establishes a robust diagnostic tool, for differentiating known lineages of A. psidii based biological predictions. This tool holds promise for detecting new pathogen incursions and can be refined for broader applications, including air-sample detection and mixed-isolate infections.

plant biology↗

bioRxiv2021

Inferring species compositions of complex fungal communities from long- and short-read sequence data

BackgroundThe kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. ResultsWe compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. ConclusionOverall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

bioinformatics↗