Hemipteroid insects (Paraneoptera), with over 10% of all known insect diversity, are a major component of terrestrial and aquatic ecosystems. Previous phylogenetic analyses have not consistently resolved the relationships among major hemipteroid lineages. We provide maximum likelihood-based phylogenomic analyses of a taxonomically comprehensive dataset comprising sequences of 2,395 single-copy, protein-coding genes for 193 samples of hemipteroid insects and outgroups. These analyses yield a well-supported phylogeny for hemipteroid insects. Monophyly of each of the three hemipteroid orders (Psocodea, Thysanoptera, and Hemiptera) is strongly supported, as are most relationships among suborders and families. Thysanoptera (thrips) is strongly supported as sister to Hemiptera. However, as in a recent large-scale analysis sampling all insect orders, trees from our data matrices support Psocodea (bark lice and parasitic lice) as the sister group to the holometabolous insects (those with complete metamorphosis). In contrast, four-cluster likelihood mapping of these data does not support this result. A molecular dating analysis using 23 fossil calibration points suggests hemipteroid insects began diversifying before the Carboniferous, over 365 million years ago. We also explore implications for understanding the timing of diversification, the evolution of morphological traits, and the evolution of mitochondrial genome organization. These results provide a phylogenetic framework for future studies of the group.

BackgroundOrthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms’ gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses.ResultsWe present a new software pipeline, Orthograph, that addresses and solves the above problems and implements useful features for a wide range of comparative genomic and transcriptomic analyses. Orthograph applies a best reciprocal hit search strategy using profile hidden Markov models and maps nucleotide sequences to the globally best matching cluster of orthologous genes, thus enabling researchers to conveniently and reliably delineate orthologs and paralogs from transcriptomic and genomic sequence data. We demonstrate the performance of our approach on de novo-sequenced and assembled transcript libraries of 24 species of apoid wasps (Hymenoptera: Aculeata) as well as on published genomic datasets.ConclusionWith Orthograph, we implemented a best reciprocal hit approach to reference-based orthology prediction for coding nucleotide sequences such as RNAseq data. Orthograph is flexible, easy to use, open source and freely available at https://mptrsen.github.io/Orthograph. Additionally, we release 24 de novo-sequenced and assembled transcript libraries of apoid wasp species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1529-8) contains supplementary material, which is available to authorized users.

Background The latest advancements in DNA sequencing technologies have facilitated the resolution of the phylogeny of insects, yet parts of the tree of Holometabola remain unresolved. The phylogeny of Neuropterida has been extensively studied, but no strong consensus exists concerning the phylogenetic relationships within the order Neuroptera. Here, we assembled a novel transcriptomic dataset to address previously unresolved issues in the phylogeny of Neuropterida and to infer divergence times within the group. We tested the robustness of our phylogenetic estimates by comparing summary coalescent and concatenation-based phylogenetic approaches and by employing different quartet-based measures of phylogenomic incongruence, combined with data permutations. Results Our results suggest that the order Raphidioptera is sister to Neuroptera + Megaloptera. Coniopterygidae is inferred as sister to all remaining neuropteran families suggesting that larval cryptonephry could be a ground plan feature of Neuroptera. A clade that includes Nevrorthidae, Osmylidae, and Sisyridae (i.e. Osmyloidea) is inferred as sister to all other Neuroptera except Coniopterygidae, and Dilaridae is placed as sister to all remaining neuropteran families. Ithonidae is inferred as the sister group of monophyletic Myrmeleontiformia. The phylogenetic affinities of Chrysopidae and Hemerobiidae were dependent on the data type analyzed, and quartet-based analyses showed only weak support for the placement of Hemerobiidae as sister to Ithonidae + Myrmeleontiformia. Our molecular dating analyses suggest that most families of Neuropterida started to diversify in the Jurassic and our ancestral character state reconstructions suggest a primarily terrestrial environment of the larvae of Neuropterida and Neuroptera. Conclusion Our extensive phylogenomic analyses consolidate several key aspects in the backbone phylogeny of Neuropterida, such as the basal placement of Coniopterygidae within Neuroptera and the monophyly of Osmyloidea. Furthermore, they provide new insights into the timing of diversification of Neuropterida. Despite the vast amount of analyzed molecular data, we found that certain nodes in the tree of Neuroptera are not robustly resolved. Therefore, we emphasize the importance of integrating the results of morphological analyses with those of sequence-based phylogenomics. We also suggest that comparative analyses of genomic meta-characters should be incorporated into future phylogenomic studies of Neuropterida.

The beetle superfamily Dytiscoidea, placed within the suborder Adephaga, comprises six families. The phylogenetic relationships of these families, whose species are aquatic, remain highly contentious. In particular the monophyly of the geographically disjunct Aspidytidae (China and South Africa) remains unclear. Here we use a phylogenomic approach to demonstrate that Aspidytidae are indeed monophyletic, as we inferred this phylogenetic relationship from analyzing nucleotide sequence data filtered for compositional heterogeneity and from analyzing amino-acid sequence data. Our analyses suggest that Aspidytidae are the sister group of Amphizoidae, although the support for this relationship is not unequivocal. A sister group relationship of Hygrobiidae to a clade comprising Amphizoidae, Aspidytidae, and Dytiscidae is supported by analyses in which model assumptions are violated the least. In general, we find that both concatenation and the applied coalescent method are sensitive to the effect of among-species compositional heterogeneity.Four-cluster likelihood-mapping suggests that despite the substantial size of the dataset and the use of advanced analytical methods, statistical support is weak for the inferred phylogenetic placement of Hygrobiidae. These results indicate that other kinds of data (e.g. genomic meta-characters) are possibly required to resolve the above-specified persisting phylogenetic uncertainties. Our study illustrates various data-driven confounding effects in phylogenetic reconstructions and highlights the need for careful monitoring of model violations prior to phylogenomic analysis. to coordination of taxon sampling and transcriptome sequencing. BM, DRM, MB, ON, RGB, and XZ, contributed to funding acquisition. DRM, DTB, FJ, HEE, KM, LH, MB, RSP, YA, and XZ collected samples and/or contributed to the data processing of the sequenced transcriptomes. AD, AV, JMP, LP, and SL performed the de novo transcriptome assembly and cross-contamination checks. AD, AV, and JMP performed the NCBI sequence submissions. AV, ON, and RMW performed the orthology inference and orthology assignment analyses. AV performed the phylogenetic analyses with contributions, suggestions and comments from BM, KM, and CM. AV, BM, ON, MB and RGB wrote the first draft of the manuscript, with AV taking the lead. All authors contributed with comments and suggestions on later versions of the manuscript. Declarations of interest: none Appendix A. Supplementary materialSupplementary data associated with this article can be found, in the online version, at (doi link upon acceptance). The filtered and unfiltered COGs as well as all inferred matrices and their partition files are available at the MENDELEY DATA repository (XXXXX).

The Syrphoidea (families Pipunculidae and Syrphidae) has been suggested to be the sister group of the Schizophora, the largest species radiation of true flies. A major challenge in dipterology is inferring the phylogenetic relationship between Syrphoidea and Schizophora in order to understand the evolutionary history of flies. Using newly sequenced transcriptomic data of Syrphidae, Pipunculidae and closely related lineages, we were able to fully resolve phylogenetic relationships of Syrphoidea using a supermatrix approach with more than 1 million amino acid positions derived from 3145 genes, including 19 taxa across nine families. Platypezoidea were inferred as a sister group to Eumuscomorpha, which was recovered monophyletic. While Syrphidae were also found to be monophyletic, the superfamily Syrphoidea was not recovered as a monophyletic group, as Pipunculidae were inferred as sister group to Schizophora. Within Syrphidae, the subfamily Microdontinae was resolved as sister group to the remaining taxa, Syrphinae and Pipizinae were placed as sister groups, and the monophyly of Eristalinae was not recovered. Although our results are consistent with previously established hypotheses on Eumuscomorphan evolution, our approach is new to dipteran phylogeny, using larger‐scale transcriptomic data for the first time for this insect group.