﻿Morphological characteristics and phylogenetic evidence reveal two new species of <i>Acremonium</i> (Hypocreales, Sordariomycetes)

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Abstract

Using chicken feathers as bait, Acremonium globosisporum sp. nov. and Acremonium curvum sp. nov. were collected from the soil of Yuncheng East Garden Wildlife Zoo and Zhengzhou Zoo in China. They were identified by combining the morphological characteristics and the two-locus DNA sequence (LSU and ITS) analyses. In the phylogenetic tree, both new species clustered into separate subclades, respectively. They were different from their allied species in their morphology. The description, illustrations, and phylogenetic tree of the two new species were provided.

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MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

Kazutaka Katoh, Daron Standley (2013)

We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.

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

Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)

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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MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

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Author and article information

Contributors

Yan-Feng Han

Journal

Journal ID (nlm-ta): MycoKeys

Journal ID (iso-abbrev): MycoKeys

Journal ID (publisher-id): 11

Journal ID (index): urn:lsid:arphahub.com:pub:C004A564-9D6A-5F9F-B058-6A3815DFE9C3

Title: MycoKeys

Publisher: Pensoft Publishers

ISSN (Print): 1314-4057

ISSN (Electronic): 1314-4049

Publication date Collection: 2022

Publication date (Electronic): 15 July 2022

Volume: 91

Pages: 85-96

Affiliations

[1 ] Institute of Fungus Resources, Department of Ecology, College of Life Sciences, Guizhou University, Guiyang 550025, Guizhou, China Guizhou University Guiyang China

[2 ] Basic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, Guizhou, China Guizhou University of Traditional Chinese Medicine Guiyang China

[3 ] Engineering Research Center of Industrial Microbiology, Ministry of Education, Fujian Normal University, Fuzhou 350108, Fujian, China Fujian Normal University Fuzhou China

Author notes

Corresponding author: Yan-Feng Han ( swallow1128@ 123456126.com )

Academic editor: Andrew Miller

Author information

Xin Li https://orcid.org/0000-0001-7910-1469

Yu-Lian Ren https://orcid.org/0000-0003-2424-1266

Wan-Hao Chen https://orcid.org/0000-0001-7240-6841

Jian-Dong Liang https://orcid.org/0000-0002-3939-3900

Article

Publisher ID: 86257

DOI: 10.3897/mycokeys.91.86257

PMC ID: 9849060

PubMed ID: 36760887

SO-VID: 47ef6be1-5334-41b7-8dcc-9ba66ab821cf

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 07 May 2022

Date accepted : 04 July 2022

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