New taxonomic framework for Arthrodermataceae: a comprehensive analysis based on their phylogenetic reconstruction, divergence time estimation, phylogenetic split network, and phylogeography

The Arthrodermataceae, or dermatophytes, are a major family in the Onygenales and important from a public health safety perspective. Here, based on sequenced and downloaded from GenBank sequences, the evolutionary relationships of Arthrodermataceae were comprehensively studied via phylogenetic reconstruction, divergence time estimation, phylogenetic split network, and phylogeography analysis. These results showed the clades Ctenomyces, Epidermophyton, Guarromyces, Lophophyton, Microsporum, Paraphyton, and Trichophyton were all monophyletic groups, whereas Arthroderma and Nannizzia were polyphyletic. Among them, Arthroderma includes at least four different clades, Arthroderma I, III and IV are new clades in Arthrodermataceae. Nannizzia contains at least two different clades, Nannizzia I and Nannizzia II, but Nannizzia II was a new clade in Arthrodermataceae. The unclassified group, distributed in Japan and India, was incorrectly identified; it should be a new clade in Arthrodermataceae. The phylogenetic split network based on the ITS sequences provided strong support for the true relationships among the lineages in the reconstructed phylogenetic tree. A haplotype phylogenetic network based on the ITS sequences was used to visualize species evolution and geographic lineages relationships in all genera except Trichophyton. The new framework provided here for the phylogeny and taxonomy of Arthrodermataceae will facilitate the rapid identification of species in the family, which should useful for evaluating the results of preventive measures and interventions, as well as for conducting epidemiological studies.

SPECTRE: a suite of phylogenetic tools for reticulate evolution

Summary

Split-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here, we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing environments.

Availability and implementation

Full source code is available under the GPLv3 license at: https://github.com/maplesond/SPECTRE. SPECTRE's core library is available from Maven Central at: https://mvnrepository.com/artifact/uk.ac.uea.cmp.spectre/core. Documentation is available at: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/.

Contact

sarah.bastkowski@earlham.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enumerating all maximal frequent subtrees in collections of phylogenetic trees

Background

A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events.

Results

We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/.

Conclusions

Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees.