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The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models t...

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Autores principales: Wang, Zheng, Kim, Wonyong, Wang, Yen-Wen, Yakubovich, Elizabeta, Dong, Caihong, Trail, Frances, Townsend, Jeffrey P., Yarden, Oded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512317/
https://www.ncbi.nlm.nih.gov/pubmed/37746130
http://dx.doi.org/10.3389/ffunb.2023.1214537
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author Wang, Zheng
Kim, Wonyong
Wang, Yen-Wen
Yakubovich, Elizabeta
Dong, Caihong
Trail, Frances
Townsend, Jeffrey P.
Yarden, Oded
author_facet Wang, Zheng
Kim, Wonyong
Wang, Yen-Wen
Yakubovich, Elizabeta
Dong, Caihong
Trail, Frances
Townsend, Jeffrey P.
Yarden, Oded
author_sort Wang, Zheng
collection PubMed
description Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.
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spelling pubmed-105123172023-09-22 The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics Wang, Zheng Kim, Wonyong Wang, Yen-Wen Yakubovich, Elizabeta Dong, Caihong Trail, Frances Townsend, Jeffrey P. Yarden, Oded Front Fungal Biol Fungal Biology Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom. Frontiers Media S.A. 2023-06-30 /pmc/articles/PMC10512317/ /pubmed/37746130 http://dx.doi.org/10.3389/ffunb.2023.1214537 Text en Copyright © 2023 Wang, Kim, Wang, Yakubovich, Dong, Trail, Townsend and Yarden https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Fungal Biology
Wang, Zheng
Kim, Wonyong
Wang, Yen-Wen
Yakubovich, Elizabeta
Dong, Caihong
Trail, Frances
Townsend, Jeffrey P.
Yarden, Oded
The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title_full The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title_fullStr The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title_full_unstemmed The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title_short The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics
title_sort sordariomycetes: an expanding resource with big data for mining in evolutionary genomics and transcriptomics
topic Fungal Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512317/
https://www.ncbi.nlm.nih.gov/pubmed/37746130
http://dx.doi.org/10.3389/ffunb.2023.1214537
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