Fungal taxonomy and ecology have been revolutionized by the application of molecular methods and both have increasing connections to genomics and functional biology. However, data streams from traditional specimen- and culture-based systematics are not yet fully integrated with those from metagenomic and metatranscriptomic studies, which limits understanding of the taxonomic diversity and metabolic properties of fungal communities. This article reviews current resources, needs, and opportunities for sequence-based classification and identification (SBCI) in fungi as well as related efforts in prokaryotes. To realize the full potential of fungal SBCI it will be necessary to make advances in multiple areas. Improvements in sequencing methods, including long-read and single-cell technologies, will empower fungal molecular ecologists to look beyond ITS and current shotgun metagenomics approaches. Data quality and accessibility will be enhanced by attention to data and metadata standards and rigorous enforcement of policies for deposition of data and workflows. Taxonomic communities will need to develop best practices for molecular characterization in their focal clades, while also contributing to globally useful datasets including ITS. Changes to nomenclatural rules are needed to enable validPUBLICation of sequence-based taxon descriptions. Finally, cultural shifts are necessary to promote adoption of SBCI and to accord professional credit to individuals who contribute to community resources.
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http://dx.doi.org/10.3852/16-130 | DOI Listing |
Nat Commun
January 2025
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
In a phylogeny, trustworthy reliability branch support estimates are as important as the tree itself. We show that reliability support values based on bootstrapping can be improved by combining sequence and structural information from proteins. Our approach relies on the systematic comparison of homologous intra-molecular structural distances.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
January 2025
Department of Microbiology and Biotechnology, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr.18, 22609, Hamburg, Germany.
The focus on microalgae for applications in several fields, e.g. resources for biofuel, the food industry, cosmetics, nutraceuticals, biotechnology, and healthcare, has gained increasing attention over the last decades.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources of China; Key Laboratory of Marine Genetic Resources of Fujian Province, Xiamen 361005, PR China.
mSphere
December 2024
Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Unlabelled: Thousands of complete genome sequences for strains of a species that are now available enable the advancement of pangenome analytics to a new level of sophistication. We collected 2,377 publicly available complete genomes of for detailed pangenome analysis. The core genome and accessory genomes consisted of 2,398 and 5,182 genes, respectively.
View Article and Find Full Text PDFComput Biol Med
December 2024
College of Food and Biological Engineering, Chengdu University, Chengdu, 610106, China; Country Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu, 610106, China.
Thermophilic proteins, mesophiles proteins and psychrophilic proteins have wide industrial applications, as enzymes with different optimal temperatures are often needed for different purposes. Convenient methods are needed to determine the optimal temperatures for proteins; however, laboratory methods for this purpose are time-consuming and laborious, and existing machine learning methods can only perform binary classification of thermophilic and non-thermophilic proteins, or psychrophilic and non-psychrophilic proteins. Here, we developed a deep learning model, PSTP-BERT, based on protein sequences that can directly perform Three classes identification of thermophilic, mesophilic, and psychrophilic proteins.
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