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Article

Gene Selection and Evolutionary Modeling Affect Phylogenomic Inference of Neuropterida Based on Transcriptome Data

1
College of Plant Protection, Hebei Agricultural University, Baoding 071001, China
2
Department of Entomology, China Agricultural University, Beijing 100193, China
3
Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
4
Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou 510642, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(5), 1072; https://doi.org/10.3390/ijms20051072
Submission received: 24 December 2018 / Revised: 18 February 2019 / Accepted: 26 February 2019 / Published: 1 March 2019
(This article belongs to the Section Molecular Genetics and Genomics)

Abstract

Neuropterida is a super order of Holometabola that consists of the orders Megaloptera (dobsonflies, fishflies, and alderflies), Neuroptera (lacewings) and Raphidioptera (snakeflies). Several proposed higher-level relationships within Neuropterida, such as the relationships between the orders or between the families, have been extensively debated. To further understand the evolutionary history of Neuropterida, we conducted phylogenomic analyses of all 13 published transcriptomes of the neuropterid species, as well as of a new transcriptome of the fishfly species Ctenochauliodes similis of Liu and Yang, 2006 (Megaloptera: Corydalidae: Chauliodinae) that we sequenced. Our phylogenomic data matrix contained 1392 ortholog genes from 22 holometabolan species representing six families from Neuroptera, two families from Raphidioptera, and two families from Megaloptera as the ingroup taxa, and nine orders of Holometabola as outgroups. Phylogenetic reconstruction was performed using both concatenation and coalescent-based approaches under a site-homogeneous model as well as under a site-heterogeneous model. Surprisingly, analyses using the site-homogeneous model strongly supported a paraphyletic Neuroptera, with Coniopterygidae assigned as the sister group of all other Neuropterida. In contrast, analyses using the site-heterogeneous model recovered Neuroptera as monophyletic. The monophyly of Neuroptera was also recovered in concatenation and coalescent-based analyses using genes with stronger phylogenetic signals [i.e., higher average bootstrap support (ABS) values and higher relative tree certainty including all conflicting bipartitions (RTCA) values] under the site-homogeneous model. The present study illustrated how both data selection and model selection influence phylogenomic analyses of large-scale data matrices comprehensively.
Keywords: transcriptome; phylogenomics; site-heterogeneous model; Neuropterida transcriptome; phylogenomics; site-heterogeneous model; Neuropterida

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MDPI and ACS Style

Wang, Y.; Zhou, X.; Wang, L.; Liu, X.; Yang, D.; Rokas, A. Gene Selection and Evolutionary Modeling Affect Phylogenomic Inference of Neuropterida Based on Transcriptome Data. Int. J. Mol. Sci. 2019, 20, 1072. https://doi.org/10.3390/ijms20051072

AMA Style

Wang Y, Zhou X, Wang L, Liu X, Yang D, Rokas A. Gene Selection and Evolutionary Modeling Affect Phylogenomic Inference of Neuropterida Based on Transcriptome Data. International Journal of Molecular Sciences. 2019; 20(5):1072. https://doi.org/10.3390/ijms20051072

Chicago/Turabian Style

Wang, Yuyu, Xiaofan Zhou, Liming Wang, Xingyue Liu, Ding Yang, and Antonis Rokas. 2019. "Gene Selection and Evolutionary Modeling Affect Phylogenomic Inference of Neuropterida Based on Transcriptome Data" International Journal of Molecular Sciences 20, no. 5: 1072. https://doi.org/10.3390/ijms20051072

APA Style

Wang, Y., Zhou, X., Wang, L., Liu, X., Yang, D., & Rokas, A. (2019). Gene Selection and Evolutionary Modeling Affect Phylogenomic Inference of Neuropterida Based on Transcriptome Data. International Journal of Molecular Sciences, 20(5), 1072. https://doi.org/10.3390/ijms20051072

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