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

Big Data Fusarium Neurospora Sordariomycetes Trichoderma evolution genomics transcriptomics

Journal

Frontiers in fungal biology
ISSN: 2673-6128
Titre abrégé: Front Fungal Biol
Pays: Switzerland
ID NLM: 9918266300506676

Informations de publication

Date de publication:
2023
Historique:
received: 30 04 2023
accepted: 06 06 2023
medline: 25 9 2023
pubmed: 25 9 2023
entrez: 25 9 2023
Statut: epublish

Résumé

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

Identifiants

pubmed: 37746130
doi: 10.3389/ffunb.2023.1214537
pmc: PMC10512317
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

1214537

Informations de copyright

Copyright © 2023 Wang, Kim, Wang, Yakubovich, Dong, Trail, Townsend and Yarden.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Zheng Wang (Z)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.

Wonyong Kim (W)

Korean Lichen Research Institute, Sunchon National University, Suncheon, Republic of Korea.

Yen-Wen Wang (YW)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.

Elizabeta Yakubovich (E)

Department of Plant Pathology and Microbiology, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.

Caihong Dong (C)

Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.

Frances Trail (F)

Department of Plant Biology, Michigan State University, East Lansing, MI, United States.
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States.

Jeffrey P Townsend (JP)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.
Department of Ecology and Evolutionary Biology, Program in Microbiology, and Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.

Oded Yarden (O)

Department of Plant Pathology and Microbiology, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.

Classifications MeSH