Morphology and Phylogeny Reveal Vamsapriyaceae fam. nov. (Xylariales, Sordariomycetes) with Two Novel Vamsapriya Species

Sun, Ya-Ru; Liu, Ning-Guo; Samarakoon, Milan C.; Jayawardena, Ruvishika S.; Hyde, Kevin D.; Wang, Yong

doi:10.3390/jof7110891

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…The taxonomic scheme of the class Sordariomycetes has been updated continuously (Maharachchikumbura et al 2015;Hyde et al 2020;Wijayawardene et al 2022), whereas the NCBI taxonomy database (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=147550) does not keep up with the updates, and some genomes were assigned incorrect lineage information (Shen et al 2020;. To ensure the correctness of the taxonomic positions of selected genomes, a taxonomic framework table composed of all generic names in Sordariomycetes and the parent lineage information, was prepared according to the taxonomic outline (Wijayawardene et al 2022), and some changes were added in keeping with the latest literature (Crous et al 2021;Sun et al 2021, Magyar et al 2022, Sugita & Tanaka 2022. We used the "Ascomycota" as the search term in NCBI's Genome Browser (https://www.ncbi.nlm.nih.gov/data-hub/genome/?taxon=4890, 12 August 2022) to obtain all records of Ascomycota genomes, and then a table, including assembly accession, organism name, strain identifiers, assemble level, and release date, was downloaded.…”

Section: Genome Collectionmentioning

confidence: 99%

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Chen

Stadler³

et al. 2023

Preprint

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Economically and agriculturally important fungal species have various lifestyles, and they may shift from mutualistic or saprobic to pathogenic depending on the habitat, host tolerance, and resource availability. Traditionally, the determination of fungal lifestyles has been based on observation at a particular host or habitat. Therefore, potential fungal pathogens have been neglected until they cause devastating impacts on human health, food security, and ecosystem stability. This study focused on the class Sordariomycetes to explore the genomic traits that could be used to determine the lifestyles of fungi and the possibility of predicting fungal lifestyles using machine learning algorithms. A total of 638 representative genomes covering five subclasses, 17 orders and 50 families were selected and annotated. Through an extensive literature survey, the lifestyles of 555 genomes were determined, including plant pathogens, saprotrophs, entomopathogens, mycoparasites, endophytes, human pathogens and nematophagous fungi. We evaluated the influence of sequencing technologies and concluded that second sequencing technologies have no influence on genome completeness but tend to generate a reduced size of transposable elements. We constructed three numerical matrices: a basic genomic feature matrix including 25 features; a functional protein matrix including 24 features; and a combined matrix. The most comprehensively comparative analysis to date across multiple lifestyles was conducted based on these matrices. Results indicate that basic genomic features reflect more on phylogeny rather than lifestyle, but the abundance of functional proteins displays relatively high discrimination not only in differentiating taxonomic groups at the higher levels but also in differentiating lifestyles. Genome size, GC content and gene number showed powerful discrimination for differentiating higher ranks, especially at the subclass level. Plant pathogens have the largest secretome; whereas entomopathogens have the smallest secretome; and the abundance of secretomes is a useful indicator to clearly differentiate plant pathogens from entomopathogens, mycoparasites, saprotrophs and entomopathogens, and as well as differentiate entophytes from entomopathogens. Effectors have long been considered as disease determinants, and we did observe that plant pathogens have more effectors than saprotrophs and entomopathogens. However, we also observed a similar abundance of effectors in endophytes, suggesting that effectors maybe not a reliable indicator for pathogenic fungi. Single functional protein could not differentiate all lifestyles, but combinations of multiple numerical features of functional proteins result in accurate differentiation for most lifestyles. Furthermore, models of six machine learning algorithms were trained, optimized and evaluated, and the best-performance model was used to predict the lifestyle of 83 unlabeled genomes. Although the accuracy of the best machine learning model was limited by the inadequate genome number of several lifestyles and the inaccurate lifestyle assignments for some genomes, the predictive model still obtained a high degree of accuracy in differentiating plant pathogens. The predictive model can be further optimized with more sequenced genomes in the future, and provide a more reliable prediction. This can be used as an early warning system to identify potentially devastating fungi and take appropriate measures to prevent their spread.

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Section: Genome Collectionmentioning

confidence: 99%

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Chen

Stadler³

et al. 2023

Preprint

Self Cite

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Taxonomy, phylogeny, molecular dating and ancestral state reconstruction of Xylariomycetidae (Sordariomycetes)

et al. 2022

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Xylariomycetidae ( Ascomycota ) is a highly diversi ed group with variable stromatic characters. Our research focused on inconspicuous stromatic xylarialean taxa from China, Italy, Russia, Thailand and the United Kingdom. Detailed morphological descriptions, illustrations and combined ITS-LSU-rpb 2-tub 2-tef 1 phylogenies revealed 38 taxa from our collections belonging to Amphisphaeriales and Xylariales . A new family ( Appendicosporaceae ), ve new genera (

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Ascomycetes from karst landscapes of Guizhou Province, China

Zhang,

Liu,

Hyde

et al. 2023

Fungal Diversity

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Morphology and Phylogeny Reveal Vamsapriyaceae fam. nov. (Xylariales, Sordariomycetes) with Two Novel Vamsapriya Species

Cited by 8 publications

References 40 publications

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Taxonomy, phylogeny, molecular dating and ancestral state reconstruction of Xylariomycetidae (Sordariomycetes)

Ascomycetes from karst landscapes of Guizhou Province, China

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