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Phylogenomic analyses of bat subordinal relationships based on transcriptome data

Abstract

Bats, order Chiroptera, are one of the largest monophyletic clades in mammals. Based on morphology and behaviour bats were once differentiated into two suborders Megachiroptera and Microchiroptera Recently, researchers proposed alternative views of chiropteran classification (suborders Yinpterochiroptera and Yangochiroptera) based on morphological, molecular and fossil evidence. Since genome-scale data can significantly increase the number of informative characters for analysis, transcriptome RNA-seq data for 12 bat taxa were generated in an attempt to resolve bat subordinal relationships at the genome level. Phylogenetic reconstructions were conducted using up to 1470 orthologous genes and 634,288 aligned sites. We found strong support for the Yinpterochiroptera-Yangochiroptera classification. Next, we built expression distance matrices for each species and reconstructed gene expression trees. The tree is highly consistent with sequence-based phylogeny. We also examined the influence of taxa sampling on the performance of phylogenetic methods, and found that the topology is robust to sampling. Relaxed molecular clock estimates the divergence between Yinpterochiroptera and Yangochiroptera around 63 million years ago. The most recent common ancestor of Yinpterochiroptera, corresponding to the split between Rhinolophoidea and Pteropodidae (Old World Fruit bats), is estimated to have occurred 60 million years ago. Our work provided a valuable resource to further explore the evolutionary relationship within bats.

Introduction

Bats belong to the order Chiroptera, one of the largest monophyletic clades in mammals. They constitute ~20% of living mammalian species, arranged in 20 families1. Their wings make bats distinct among mammals2. Living bats had been placed in one of two suborders based on morphology and behaviour3. All bats that produce echolocation calls in their larynges were placed in the suborder Microchiroptera4. All other bats were placed in the suborder Megachiroptera (Old World fruit bats, non-echolocating bats). However, a reconsideration of morphological, behavioural and molecular evidence demonstrates that there are two suborders of bats, Yinpterochiroptera and Yangochiropterathat do not coincide with the previous subordinal classificaiton5,6. The two new suborders are strongly supported by statistical tests. Phylogenomic analysis based on genome sequencing data support the classification of living bats in Yinpterochiroptera and Yangochiroptera7.

Recently, O’Leary et al. claimed that living echolocating bats were monophyletic8. They based this on morphological data set and published molecular sequence data Although the bat genome data set is rich in the number of loci, it is not comprehensive in taxon sampling, an important component for accurately estimating phylogeny9. We investigated the evolutionary relationships of bats based on more taxa at the genome level. Because regulatory changes affecting gene expression might explain many or even most phenotypic differences between species10, we made between-species comparisons at sequence and expression levels. We generated transcriptome data for 12 bat taxa and used data from two published bat genomes11. The bat transcriptome data we present is largely expanded the coverage across the bat clade. After evaluating the influence of taxa sampling on the performance of phylogenetic methods, we found strong support for the Yinpterochiroptera-Yangochiroptera classification. Furthermore, the expression-based tree is consistent with sequence-based phylogeny. These results provided a phylogenetic framework and timescale with which to interpret the evolution of bats.

Results

Transcriptome sequencing and assembly

In this work, we sequenced cDNAs from 12 bat species and generated 610 million raw reads (Table 1). After sequencing reads filter steps, a total of 595 million clean reads was obtained. Next, the clean reads were de novo assembled using trinity package12, and a summary of the assembly statistics is shown in Table 1. We excluded all contigs less than 200 bp from further analysis, and finally obtained a total of 1,993,822 contigs (82348–254130 per sample). Next, we performed redundancy reduction on the raw assemblies, processing them to identify candidate open reading frames (ORF) within the transcripts. We chose the longest ORF was chosen and selected one peptide per putative unigene. At last, 32,227–51,271 we retained peptide sequences per taxon

Table 1 Global statistic of de novo brain transcriptome assembles.

Phylogenomics analyses and molecular dating

Along with 18 previously published mammalian genome sequences, a total of 1470 1:1 orthologous was obtained across 30 species (14 bat species and 16 other mammals). We performed multiple sequence alignments and our aligned supermatrix included 634,288 amino acids. We first used concatenated nucleotide and protein sequences using maximum likelihood to reconstruct the phylogeny. As with previous works13, Yinpterochiroptera and Yangochiroptera received 100% bootstrap support based on the nucleotide and protein supermatrices (Fig. 1). The rhinolophoid bats are sister group to the Old World fruit bats within Yinpterochiroptera. This result is also strongly supported by coalescent analyses (Fig. S1). All relationships within Yinpterochiroptera and Yangochiroptera were congruent with previous study13 with 100% bootstrap support (Fig. 1). Next, we measured the gene expression phylogeny within bats. We built expression distance matrices for each species and reconstructed gene expression trees. As shown in Fig. 2, the gene expression-based tree is highly consistent with the sequence-based phylogeny.

Figure 1: Maximum likelihood phylogenetic tree for nucleotide and amino acid datasets with bootstrap support values (1000 replicates) under partition model.
figure 1

The asterisks indicate 100/100 bootstrap support for nucleotide and amino acid datasets respectively.

Figure 2: Bat gene expression phylogeny.
figure 2

Neighbor-joining tree based on pairwise distance matrices for brain transcriptome.

We obtained a disagreement using nucleotide and amino acid sequences when addressing the position of bats within the superorder Laurasiatheria. The nucleotide tree recovered Pegasoferae group (Chiroptera + Perissodactyla with 95% bootstrap value support), whereas amino acid tree supported that bats are a sister group to Fereuungulata group (Carnivores + Perissodactyla + Cetariodactyla with 100% bootstrap value support) (Fig. S2). Previous works have published eight proposed higher clades within Laurasiatheria (Fig. 3). To dissect the phylogenetic signal, we measured the relative support of each locus for the evolutionary relationships of bats. The approximately unbiased (AU) test statistics analyses of eight potential topologies suggested that all Microchiroptera-Megachiroptera topologies were significantly rejected (P-value < 0.05, Table 2), and four potential Yinpterochiroptera-Yangochirptera topologies could not be significantly rejected.

Figure 3
figure 3

Eight proposed species tree topologies differing in the position of bats within mammals.

Table 2 Approximately Unbiased test for 16 hypothetical species topologies.

To evaluate the influence of the taxa sampling on phylogenetic reconstruction, analyses were performed from different subsets of taxa. We constructed concatenated trees for different taxa sets that include at least two Old World fruit bat, two rhinolophoid bats and two Yangochiroptera. Phylogeny analyses assigned high support (bootstrap value >90%) based on different sampling datasets. As shown in Fig. 4, both the results of concatenation and coalescence analyses give consistent phylogenetic estimation of relationships and support the Yinpterochiroptera-Yangochirptera topologies (concatenated method: 2% for nucleotide and 15.5% for amino acids; coalescent method: 5% for nucleotide and 12% for amino acids, respectively).

Figure 4: Influence of taxon sampling on the performance of phylogenetic trees.
figure 4

Both concatenated and coalescent analyses yield consistent phylogeny of Yinpterochiroptera-Yangochirptera classification.

Results of divergence time estimation carried out under the auto-correlated models of molecular clock relaxation are shown in Table 3. The results varied depending on the models and datasets used, but were nevertheless consistent between PhyloBayes and MCMCTree approaches. The result of MCMCTree WAG + G model is consistent with previous findings13 and values from TimeTree database14. We considered that the estimation of divergence time obtained from this model are the most reliable. We estimated the origin of Chiroptera at 63 Myr (million years ago), following the Cretaceous-Tertiary boundary. The divergence of Pteropodidae and Rhinolophoidea was estimated to be 60 Myr, earlier than previous suggestions13. We estimated the most recent common ancestor of Rhinolophidae and Hipposideridae at 40 Myr.

Table 3 Results of Bayesian relaxed molecular clock analyses.

Discussion

The development of next-generation sequencing technologies have generated many more genome sequences. This has allowed reconstruction of phylogenetic trees based on genome scale data providing a powerful approach to resolve the evolutionary relationships15,16. Transcriptome data are often used in the phylogenomic analyses studies for those non-model organisms without genome information17,18,19. RNA-seq technology is a high-efficiency way to obtain full-length coding sequence at a lower cost12,20. In this study, we sequenced large-scale whole brain transcriptome data for 12 bats and presented a large-scale, phylogenomic perspective to resolving the backbone phylogeny of bats using a larger taxon set.

Variation in gene expression and protein sequence can both influence phenotype21, and a better understanding of the evolutionary relationship between gene expression and protein sequence may provide great insights into the processes that ultimately contribute to phenotypic diversification. Both expression and sequence-based phylogeny support the Yinpterochiroptera-Yangochirptera subdivision, while rhinolophoid bats and Old World fruit bats form a monophyletic group. Studies have proved that taxon sampling is an important way for accurately assessing phylogenies22,23. Further improved taxon sampling gives a consistent phylogenetic estimation of relationship, and only few misleading phylogenies were generated. One caveat of our work is that no bats belonging to Noctilionoidea were included. Further analyses with the brain transcriptome data of bats belong to Noctilionoidea is needed.

The conflict of ‘species tree’ and ‘gene tree’ challenges the traditional methodology of molecular phylogeny. Between-genes phylogenetic incongruences can arise for several reasons, involving convergent evolution in Microchiroptera. The homoplastic signal in morphology within echolocating bats can also be reflected from molecular evidences. For example, the ‘hearing gene’ Prestin has been shown to have undergone sequence convergence among echolocating mammals. Recent genome analysis demonstrated that adaptive convergences are widespread at both the molecular and morphological level24. Although sequence convergence is traditionally considered to be rare, our data provided a large resource to decipher genome-wide sequence convergence within bats.

Taken together, our work generated large-scale transcriptomes of bats and analyzed bat subordinal relationships at genome-wide scale. With increased taxa sampling and sufficient numbers of loci, we can obtain a more reliable phylogeny. These data also provided valuable information for further researches related to molecular evolutionary analyses.

Materials and Methods

Ethics Statement

All animal experiments were carried out in strict accordance with the regulations for the use of laboratory animals (Decree No. 2, State Science and Technology Commission, People’s Republic of China, November 14, 1988) and were approved by the Animal Ethics Committee of East China Normal University (ID no: 20090219 and 20101002). No endangered or region-protected animal species were included, and no specific permission was required.

Sample collection and transcriptome sequencing

We collected adult specimens of 12 bat species covering 8 of the 18 extant chiropteran families, namely, three from the family Vespertilionidae (Murina leucogaster, Myotis ricketti and Scotophilus kuhlii), two from the family Pteropodldae (Cynopterus sphinx and Rousettus leschenaultii), two from the family Hipposideridae (Aselliscus stoliczkanus and Hipposideros pratti), one from the family Rhinolophidae (Rhinolophus pusillus), one from the family Megadermatidae (Megaderma lyra), one from the family Rhinopomatidae (Rhinopoma hardwickei), one from the family Emballonuridae (Taphozous melanopogon), and one from the family Molossidae (Tadarida teniotis) (Table S1). These bats were euthanized by halothane hyperanesthesia followed by thoracotomy. Efforts were devoted to minimize animal suffering. The whole brain tissues of these individuals were placed on ice immediately after sacrifice. All brain tissues were flash frozen in liquid nitrogen and kept at −80 °C freezer until processed for total RNA isolation.

Total RNA was extracted from brain tissue using Trizol (Life Technologies Corp) according to manufacturer’s protocols. RNA concentration was determined using a NanoDrop spectrophotometer, and RNA quality was assessed by Agilent Bioanalyzer. New sequence data of each bat brain were generated using the Illumina Hiseq2500 platform. Raw reads were deposited into the Short Read Archive (SRA) database of NCBI under the accession no. SRP062200.

Quality control and De novo transcriptome assembly

For each paired-end library, we first removed the adapters of raw reads. Then, the DynamicTrim Perl script in the SolexQA package25 was used to trim the poor quality positions of reads with parameters setting: ‘-b –h 15’. Next, the Trinity (version: trinityrnaseq_r20140413) software was used to de novo assembly the transcriptome of each tissue with default parameters12. The program was run on 64-bit Linux system (Red Hat 6.0) with 256 internal memory. TransDecoder, a program nested in the trinity package, was then used to identify the candidate coding sequence (CDS) from the contigs. At last, the CD-Hit program was used to reduce sequence redundancy of coding sequences with at least 95% global similarity26. All final CDSs with a length more than 200 bp were used for further analyses Assembled contigs were deposited into transcriptome shotgun assembly (TSA) database of NCBI under the accession no. SRP062200.

Phylogenomic Analyses

Except for the 12 newly sequenced bat brain transcriptome data, we also downloaded the coding sequences of other 18 mammalian species (large flying fox, little brown bat, horse, rhinoceros, cow, pig, dolphin, dog, cat, hedgehog, shrew, mouse, rat, human, chimpanzee, elephant, armadillo, opossum) from Ensembl database (Table S1). Only sequences of coding regions with the length larger than 400 bp were retained for further analysis. To obtain the orthologous genes among all the species, all-against-all reciprocal blastp search was employed. For each orthologous gene, we extracted protein sequences and their corresponding coding sequences. Multiple alignments were performed using MAFFT software with default settings27.

We generated two sequence super-matrices by concatenating aligned nucleotide and protein sequences of orthologous genes separately. For each matrix, we conducted the maximum likelihood analyses with model partitioning. The nucleotides substitution model was selected based on the result of Protest with bootstrap analyses were replicated for 100 times using RAxML program (version: 8.0.20)28.

We calculated standard RPKM expression values (that were then log2-transformed) for the orthologous genes. We constructed expression tree using the neighbor-joining approach, based on pairwise distance matrices between samples. The distance between samples was computed as 1-ρ, where ρ is Spearman’s correlation coefficient.

To compare the alternative topologies, approximately unbiased (AU) test statistic was computed using CONSEL package29. Cumulative scores of support for eight previously published species trees were calculated by counting the number of loci supporting the phylogeny based on the AU P-values at a critical value α=0.05. In addition, we randomly sampled one species for each order within Laurasiatherian (Perissodactyla, Cetartiodactyla, Carnivoa, Eulipotyphyla) and combined other species to generate nucleotide and protein sequences super-matrices. We repeatedly preformed the same phylogenetic tree reconstruction and AU-test analyses for these matrices.

Coalescent-based analyses were performed using ASTRAL method30 with the neighbor-joining algorithm on a matrix of ranks of taxon pairs in the gene trees under a GTR + Γ model. This analysis were replicated for 100 time using Phybase31.

Taxon sampling

To evaluate the influence of taxa sampling on the phylogeny, several analyses were performed from different subset of taxa using concatenated genes. Because basal lineages of major groups are crucial for phylogeny reconstruction, at least two Old World fruit bats, two rhinolophoid bats and two Yangochiroptera from the taxon set were included for each sampling analysis.

Molecular dating analyses

The divergence time was estimated using the Bayesian relaxed molecular clock approaches implemented in PhyloBayes, MCMCTree nested in PAML package32. With PhyloBayes approach, the CAT + GTR + G4 mixture model (for amino acid and nucleotide sequences), LG + G (amino acid sequences) and GTR + G (nucleotide sequences) models were employed. With MCMCTree, the standard WAG + G (amino acid sequences) and GRT + G (nucleotide sequences) models were used. For the PhyloBayes, all analyses were performed by running two independent MCMC chains from a random tree for 20,000 cycles, sampling posterior rates and dates every 10 cycles until 2,000 points were obtained. Posterior estimation of divergence times were estimated from the last 1800 samples of each chain after discarding the initial 10% burn-in periods within each MCMC run. For the MCMCTree, the first 1,000,000 replicates were discarded as burn-in, and the MCMC was run for 100,000,000 replicates, with the sampling frequency of 100 iterations.

Additional Information

How to cite this article: Lei, M. and Dong, D. Phylogenomic analyses of bat subordinal relationships based on transcriptome data. Sci. Rep. 6, 27726; doi: 10.1038/srep27726 (2016).

References

  1. 1

    Wilson, D. E. & Reeder, D. M. Mammal species of the world : a taxonomic and geographic reference. 3rd edn, (Johns Hopkins University Press, 2005).

  2. 2

    Altringham, J. D. Bats: biology and behaviour. (Oxford University Press, 1996).

  3. 3

    Gunnell, G. & Simmons, N. Fossil Evidence and the Origin of Bats. J Mammal Evol 12, 209–246, 10.1007/s10914-005-6945-2 (2005).

    Article  Google Scholar 

  4. 4

    Hill, J. E. & Smith, J. D. Bats : a natural history. 1st edn, (University of Texas Press, 1984).

  5. 5

    Springer, M. S., Teeling, E. C., Madsen, O., Stanhope, M. J. & de Jong, W. W. Integrated fossil and molecular data reconstruct bat echolocation. Proceedings of the National Academy of Sciences of the United States of America 98, 6241–6246, 10.1073/pnas.111551998 (2001).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Teeling, E. C. et al. Microbat paraphyly and the convergent evolution of a key innovation in Old World rhinolophoid microbats. Proceedings of the National Academy of Sciences of the United States of America 99, 1431–1436, 10.1073/pnas.022477199 (2002).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Tsagkogeorga, G., Parker, J., Stupka, E., Cotton, J. A. & Rossiter, S. J. Phylogenomic analyses elucidate the evolutionary relationships of bats. Current biology : CB 23, 2262–2267, 10.1016/j.cub.2013.09.014 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. 8

    O’Leary, M. A. et al. The placental mammal ancestor and the post-K-Pg radiation of placentals. Science 339, 662–667, 10.1126/science.1229237 (2013).

    Article  ADS  CAS  Google Scholar 

  9. 9

    Pollock, D. D., Zwickl, D. J., McGuire, J. A. & Hillis, D. M. Increased taxon sampling is advantageous for phylogenetic inference. Systematic biology 51, 664–671, 10.1080/10635150290102357 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10

    King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. 11

    Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482, 10.1038/nature10530 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature biotechnology 29, 644–652, 10.1038/nbt.1883 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Teeling, E. C. et al. A molecular phylogeny for bats illuminates biogeography and the fossil record. Science 307, 580–584, 10.1126/science.1105113 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. 14

    Kumar, S. & Hedges, S. B. TimeTree2: species divergence times on the iPhone. Bioinformatics 27, 2023–2024, 10.1093/bioinformatics/btr315 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Rannala, B. & Yang, Z. Phylogenetic inference using whole genomes. Annual review of genomics and human genetics 9, 217–231, 10.1146/annurev.genom.9.081307.164407 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. 16

    Song, S., Liu, L., Edwards, S. V. & Wu, S. Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model. Proceedings of the National Academy of Sciences of the United States of America 109, 14942–14947, 10.1073/pnas.1211733109 (2012).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Smith, S. A. et al. Resolving the evolutionary relationships of molluscs with phylogenomic tools. Nature 480, 364–367, 10.1038/nature10526 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  18. 18

    Cannon, J. T. et al. Phylogenomic resolution of the hemichordate and echinoderm clade. Current biology : CB 24, 2827–2832, 10.1016/j.cub.2014.10.016 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Wickett, N. J. et al. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proceedings of the National Academy of Sciences of the United States of America 111, E4859–4868, 10.1073/pnas.1323926111 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics 10, 57–63, 10.1038/nrg2484 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Carroll, S. B. Evolution at two levels: on genes and form. PLoS biology 3, e245, 10.1371/journal.pbio.0030245 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Teeling, E. C. et al. Molecular evidence regarding the origin of echolocation and flight in bats. Nature 403, 188–192, 10.1038/35003188 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. 23

    Simmons, N. B. & Geisler, J. H. Phylogenetic relationships of Icaronycteris, Archaeonycteris, Hassianycteris, and Palaeochiropteryx to extant bat lineages, with comments on the evolution of echolocation and foraging strategies in Microchiroptera. B Am Mus Nat Hist, 4–182 (1998).

  24. 24

    Parker, J. et al. Genome-wide signatures of convergent evolution in echolocating mammals. Nature 502, 228–231, 10.1038/nature12511 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. 25

    Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC bioinformatics 11, 485, 10.1186/1471-2105-11-485 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26

    Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659, 10.1093/bioinformatics/btl158 (2006).

    Article  CAS  Google Scholar 

  27. 27

    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular biology and evolution 30, 772–780, 10.1093/molbev/mst010 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313, 10.1093/bioinformatics/btu033 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Systematic biology 51, 492–508, 10.1080/10635150290069913 (2002).

    Article  PubMed  Google Scholar 

  30. 30

    Mirarab, S. et al. ASTRAL: genome-scale coalescent-based species tree estimation. Bioinformatics 30, i541–548, 10.1093/bioinformatics/btu462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Liu, L. & Yu, L. Phybase: an R package for species tree analysis. Bioinformatics 26, 962–963, 10.1093/bioinformatics/btq062 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. 32

    Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Molecular biology and evolution 24, 1586–1591, 10.1093/molbev/msm088 (2007).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This project is supported by Key Construction Program of the National ‘985’ project of East China Normal University to Dong Dong (79633006).

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D.D. designed the study. M.L. carried out the data analysis. M.L. and D.D. wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dong Dong.

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The authors declare no competing financial interests.

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Lei, M., Dong, D. Phylogenomic analyses of bat subordinal relationships based on transcriptome data. Sci Rep 6, 27726 (2016). https://doi.org/10.1038/srep27726

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