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      In honor of John Bissett: authoritative guidelines on molecular identification of Trichoderma

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      Fungal Diversity
      Springer Science and Business Media LLC

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          Abstract

          Modern taxonomy has developed towards the establishment of global authoritative lists of species that assume the standardized principles of species recognition, at least in a given taxonomic group. However, in fungi, species delimitation is frequently subjective because it depends on the choice of a species concept and the criteria selected by a taxonomist. Contrary to it, identification of fungal species is expected to be accurate and precise because it should predict the properties that are required for applications or that are relevant in pathology. The industrial and plant-beneficial fungi from the genus Trichoderma (Hypocreales) offer a suitable model to address this collision between species delimitation and species identification. A few decades ago, Trichoderma diversity was limited to a few dozen species. The introduction of molecular evolutionary methods resulted in the exponential expansion of Trichoderma taxonomy, with up to 50 new species recognized per year. Here, we have reviewed the genus-wide taxonomy of Trichoderma and compiled a complete inventory of all Trichoderma species and DNA barcoding material deposited in public databases (the inventory is available at the website of the International Subcommission on Taxonomy of Trichoderma www.trichoderma.info). Among the 375 species with valid names as of July 2020, 361 (96%) have been cultivated in vitro and DNA barcoded. Thus, we have developed a protocol for molecular identification of Trichoderma that requires analysis of the three DNA barcodes (ITS, tef1, and rpb2), and it is supported by online tools that are available on www.trichokey.info. We then used all the whole-genome sequenced (WGS) Trichoderma strains that are available in public databases to provide versatile practical examples of molecular identification, reveal shortcomings, and discuss possible ambiguities. Based on the Trichoderma example, this study shows why the identification of a fungal species is an intricate and laborious task that requires a background in mycology, molecular biological skills, training in molecular evolutionary analysis, and knowledge of taxonomic literature. We provide an in-depth discussion of species concepts that are applied in Trichoderma taxonomy, and conclude that these fungi are particularly suitable for the implementation of a polyphasic approach that was first introduced in Trichoderma taxonomy by John Bissett (1948–2020), whose work inspired the current study. We also propose a regulatory and unifying role of international commissions on the taxonomy of particular fungal groups. An important outcome of this work is the demonstration of an urgent need for cooperation between Trichoderma researchers to get prepared to the efficient use of the upcoming wave of Trichoderma genomic data.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

            Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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              AMPLIFICATION AND DIRECT SEQUENCING OF FUNGAL RIBOSOMAL RNA GENES FOR PHYLOGENETICS

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Fungal Diversity
                Fungal Diversity
                Springer Science and Business Media LLC
                1560-2745
                1878-9129
                March 2021
                February 05 2021
                March 2021
                : 107
                : 1
                : 1-69
                Article
                10.1007/s13225-020-00464-4
                770425bc-c30b-47c0-ba27-6cc6429112b5
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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