scholarly journals Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4349 ◽  
Author(s):  
Aristóteles Góes-Neto ◽  
Marcelo V.C. Diniz ◽  
Daniel S. Carvalho ◽  
Gilberto C. Bomfim ◽  
Angelo A. Duarte ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Phylogenetic Analysis ◽  
Complex Networks ◽  
Complex Network ◽  
Bootstrap Method ◽  
Molecular Data ◽  
Data Sets ◽  
Fungal Cell ◽  
Taxonomic Groups ◽  
Inference Methods

Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.

Tuberculina photiniae sp. nov. (Helicobasidiales, Basidiomycota) supported by morphological characteristics and phylogenetic data

Phytotaxa ◽  
2017 ◽  
Vol 317 (2) ◽  
pp. 113 ◽  
Author(s):  
YU-MEI ZHAO ◽  
JIN XIE ◽  
MENG-JIE LI ◽  
LING ZHU ◽  
TONG-XIN ZHOU ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
New Species ◽  
Phylogenetic Analyses ◽  
Morphological Characteristics ◽  
Molecular Data ◽  
Fungal Species ◽  
Molecular Evidence ◽  
Monophyletic Lineage ◽  
Leaf Surfaces ◽  
Inference Methods

A new mycoparasitic fungal species, Tuberculina photiniae sp. nov., is proposed based on morphological and molecular evidence. The species is characterized by its depressed hemispherical pale lilac to greyish purple sporodochia breaking through leaf surfaces, septated conidiophores with vinaceous lower parts and colourless upper parts, and the presence of colourless globose, subglobose or scarcely wide-ellipsoid conidia. Sequences of ITS and LSU nrRNA gene regions of the studied samples were generated, and phylogenetic analyses were performed with maximum likelihood, maximum parsimony and Bayesian inference methods. The phylogenetic analysis based on molecular data of ITS sequences showed that Tuberculina photiniae belongs to the Tuberculina-Helicobasidium clade and is closely related to H. longisporum and H. mompa. Further investigation was carried out for representative taxa in the Tuberculina-Helicobasidium group based on ITS+nLSU sequences in which the results demonstrated that the new species formed a monophyletic lineage and grouped with H. longisporum and H. mompa. Both morphological and molecular evidence confirmed the placement of the new species in the Tuberculina-Helicobasidium clade.

scholarly journals Combining complex networks and data mining: why and how

2016 ◽  
Author(s):  
M. Zanin ◽  
D. Papo ◽  
P. A. Sousa ◽  
E. Menasalvas ◽  
A. Nicchi ◽  
...  
Keyword(s):  
Data Mining ◽  
Complex Networks ◽  
Complex Network ◽  
Network Theory ◽  
Data Sets ◽  
Complex Network Theory ◽  
Data Mining Algorithms ◽  
Starting Point ◽  
The Face ◽  
General Goal

AbstractThe increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

The role of molecular genetics in sculpting the future of integrative biogeography

2008 ◽  
Vol 32 (2) ◽  
pp. 173-202 ◽  
Author(s):  
Brett R. Riddle ◽  
Michael N. Dawson ◽  
Elizabeth A. Hadly ◽  
David J. Hafner ◽  
Michael J. Hickerson ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Molecular Genetics ◽  
Molecular Genetic ◽  
Molecular Data ◽  
Data Sets ◽  
Late Neogene ◽  
Temporal Congruence ◽  
Lineage Divergence ◽  
Analytical Tools

We review the expanding role of molecular genetics in the emergence of a vibrant and vital integrative biogeography. The enormous growth over the past several decades in the number and variety of molecular-based phylogenetic and population genetics studies has become the core information used by biogeographers to reconstruct the causal connections between historical evolutionary and ecological attributes of taxa and biotas, and the landscapes and seascapes that contain them. A proliferation of different approaches, sequences, and genomes have provided for the integration of a `biogeography of the Late Neogene' with other Earth and biological sciences under the rubrics of phylogeography, landscape genetics, and phylochronology. Approaches designed explicitly to take advantage of unique properties of molecular genetic information have led to the re-emergence of dispersal as an analytically tractable process that historical biogeographers can now use, along with vicariance, to reconstruct the geographical context of diversification. Concomitant with the expanding amount of information available, molecular data sets often provide for estimates of lineage divergence dates, and analytical tools for doing so continue to improve. The comparability of molecular-based estimates of phylogenetic and population genetic histories across non-related taxa has stimulated deployment of new methods to test for spatial and temporal congruence across co-distributed taxa and ecosystems, and thus increased rigour in hypothesis-testing. We illustrate how a molecular genetics framework has provided robust and novel reconstructions of historical biogeographical pattern and process in three different systems, and finish with some thoughts on the role a molecular genetic-based biogeography will play in predicting alternative futures of biodiversity.

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Olivia M Gearner ◽  
Marcin J Kamiński ◽  
Kojun Kanda ◽  
Kali Swichtenberg ◽  
Aaron D Smith
Keyword(s):  
Dna Sequences ◽  
Species Group ◽  
Phylogenetic Placement ◽  
Darkling Beetles ◽  
Species Groups ◽  
Taxonomic Groups ◽  
Inference Methods ◽  
A New Species ◽  
Coleoptera Tenebrionidae

Abstract Sepidiini is a speciose tribe of desert-inhabiting darkling beetles, which contains a number of poorly defined taxonomic groups and is in need of revision at all taxonomic levels. In this study, two previously unrecognized lineages were discovered, based on morphological traits, among the extremely speciose genera Psammodes Kirby, 1819 (164 species and subspecies) and Ocnodes Fåhraeus, 1870 (144 species and subspecies), namely the Psammodes spinosus species-group and Ocnodes humeralis species-group. In order to test their phylogenetic placement, a phylogeny of the tribe was reconstructed based on analyses of DNA sequences from six nonoverlapping genetic loci (CAD, wg, COI JP, COI BC, COII, and 28S) using Bayesian and maximum likelihood inference methods. The aforementioned, morphologically defined, species-groups were recovered as distinct and well-supported lineages within Molurina + Phanerotomeina and are interpreted as independent genera, respectively, Tibiocnodes Gearner & Kamiński gen. nov. and Tuberocnodes Gearner & Kamiński gen. nov. A new species, Tuberocnodes synhimboides Gearner & Kamiński sp. nov., is also described. Furthermore, as the recovered phylogenetic placement of Tibiocnodes and Tuberocnodes undermines the monophyly of Molurina and Phanerotomeina, an analysis of the available diagnostic characters for those subtribes is also performed. As a consequence, Phanerotomeina is considered as a synonym of the newly redefined Molurina sens. nov. Finally, spectrograms of vibrations produced by substrate tapping of two Molurina species, Toktokkus vialis (Burchell, 1822) and T. synhimboides, are presented.

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