101 genomes: A test case for predicting lifestyles and emergence of pathogens.

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2020
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is the largest class of kingdom Fungi and comprises an incredible diversity of lifestyles, many of which have evolved multiple times. Plant pathogens represent a major ecological niche of the class and they are known to infect most major food crops and feedstocks for biomass and biofuel production. Studying the ecology and evolution of has significant implications for our fundamental understanding of fungal evolution, their adaptation to stress and host specificity, and practical implications with regard to the effects of climate change and on the food, feed, and livestock elements of the agro-economy. In this study, we present the first large-scale, whole-genome comparison of 101 introducing 55 newly sequenced species. The availability of whole-genome data produced a high-confidence phylogeny leading to reclassification of 25 organisms, provided a clearer picture of the relationships among the various families, and indicated that pathogenicity evolved multiple times within this class. We also identified gene family expansions and contractions across the phylogeny linked to ecological niches providing insights into genome evolution and adaptation across this group. Using machine-learning methods we classified fungi into lifestyle classes with >95 % accuracy and identified a small number of gene families that positively correlated with these distinctions. This can become a valuable tool for genome-based prediction of species lifestyle, especially for rarely seen and poorly studied species.
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Authors Haridas, S;Albert, R;Binder, M;Bloem, J;LaButti, K;Salamov, A;Andreopoulos, B;Baker, S E;Barry, K;Bills, G;Bluhm, B H;Cannon, C;Castanera, R;Culley, D E;Daum, C;Ezra, D;González, J B;Henrissat, B;Kuo, A;Liang, C;Lipzen, A;Lutzoni, F;Magnuson, J;Mondo, S J;Nolan, M;Ohm, R A;Pangilinan, J;Park, H-J;Ramírez, L;Alfaro, M;Sun, H;Tritt, A;Yoshinaga, Y;Zwiers, L-H;Turgeon, B G;Goodwin, S B;Spatafora, J W;Crous, P W;Grigoriev, I V;
Journal studies in mycology
Year 2020
DOI 10.1016/j.simyco.2020.01.003
URL
Keywords
machine-learning new taxa aulographales crous, spatafora, haridas & grigoriev coniosporiaceae crous, spatafora, haridas & grigoriev coniosporiales crous, spatafora, haridas & grigoriev eremomycetales crous, spatafora, haridas & grigoriev fungal evolution genome-based prediction lineolataceae crous, spatafora, haridas & grigoriev lineolatales crous, spatafora, haridas & grigoriev rhizodiscinaceae crous, spatafora, haridas & grigoriev

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