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dc.contributor.authorMakiola, Andreasen_NZ
dc.contributor.authorDickie, Ian A.en_NZ
dc.contributor.authorHoldaway, Robert J.en_NZ
dc.contributor.authorWood, Jamie R.en_NZ
dc.contributor.authorOrwin, Kate H.en_NZ
dc.contributor.authorLee, Charles Kai-Wuen_NZ
dc.contributor.authorGlare, Travis R.en_NZ
dc.date.accessioned2019-06-04T01:10:01Z
dc.date.available2018en_NZ
dc.date.available2019-06-04T01:10:01Z
dc.date.issued2018en_NZ
dc.identifier.citationMakiola, A., Dickie, I. A., Holdaway, R. J., Wood, J. R., Orwin, K. H., Lee, C. K., & Glare, T. R. (2018). Biases in the metabarcoding of plant pathogens using rust fungi as a model system. MicrobiologyOpen, e780. https://doi.org/10.1002/mbo3.780en
dc.identifier.issn2045-8827en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12592
dc.description.abstractPlant pathogens such as rust fungi (Pucciniales) are of global economic and ecological importance. This means there is a critical need to reliably and cost‐effectively detect, identify, and monitor these fungi at large scales. We investigated and analyzed the causes of differences between next‐generation sequencing (NGS) metabarcoding approaches and traditional DNA cloning in the detection and quantification of recognized species of rust fungi from environmental samples. We found significant differences between observed and expected numbers of shared rust fungal operational taxonomic units (OTUs) among different methods. However, there was no significant difference in relative abundance of OTUs that all methods were capable of detecting. Differences among the methods were mainly driven by the method's ability to detect specific OTUs, likely caused by mismatches with the NGS metabarcoding primers to some Puccinia species. Furthermore, detection ability did not seem to be influenced by differences in sequence lengths among methods, the most appropriate bioinformatic pipeline used for each method, or the ability to detect rare species. Our findings are important to future metabarcoding studies, because they highlight the main sources of difference among methods, and rule out several mechanisms that could drive these differences. Furthermore, strong congruity among three fundamentally different and independent methods demonstrates the promising potential of NGS metabarcoding for tracking important taxa such as rust fungi from within larger NGS metabarcoding communities. Our results support the use of NGS metabarcoding for the large‐scale detection and quantification of rust fungi, but not for confirming the absence of species.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherWileyen_NZ
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2018 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.
dc.subjectCloningen_NZ
dc.subjectIlluminaen_NZ
dc.subjectIon torrenten_NZ
dc.subjectNext-generation sequencingen_NZ
dc.subjectPlant pathogensen_NZ
dc.subjectPuccinialesen_NZ
dc.titleBiases in the metabarcoding of plant pathogens using rust fungi as a model systemen_NZ
dc.typeJournal Article
dc.identifier.doi10.1002/mbo3.780en_NZ
dc.relation.isPartOfMicrobiologyOpenen_NZ
pubs.elements-id237516
pubs.publication-statusPublished onlineen_NZ
pubs.publisher-urlhttps://www.researchgate.net/publication/329918235_Biases_in_the_metabarcoding_of_plant_pathogens_using_rust_fungi_as_a_model_system/downloaden_NZ
pubs.volumee780en_NZ


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