Insect phylogenomics: results, problems and the impact of matrix composition.

In this study, we investigated the relationships among insect orders with a main focus on Polyneoptera (lower Neoptera: roaches, mantids, earwigs, grasshoppers, etc.), and Paraneoptera (thrips, lice, bugs in the wide sense). The relationships between and within these groups of insects are difficult to resolve because only few informative molecular and morphological characters are available. Here, we provide the first phylogenomic expressed sequence tags data ('EST': short sub-sequences from a c(opy) DNA sequence encoding for proteins) for stick insects (Phasmatodea) and webspinners (Embioptera) to complete published EST data. As recent EST datasets are characterized by a heterogeneous distribution of available genes across taxa, we use different rationales to optimize the data matrix composition. Our results suggest a monophyletic origin of Polyneoptera and Eumetabola (Paraneoptera + Holometabola). However, we identified artefacts of tree reconstruction (human louse Pediculus humanus assigned to Odonata (damselflies and dragonflies) or Holometabola (insects with a complete metamorphosis); mayfly genus Baetis nested within Neoptera), which were most probably rooted in a data matrix composition bias due to the inclusion of sequence data of entire proteomes. Until entire proteomes are available for each species in phylogenomic analyses, this potential pitfall should be carefully considered.

Potential pitfalls of modelling ribosomal RNA data in phylogenetic tree reconstruction: evidence from case studies in the Metazoa.

BACKGROUND: Failure to account for covariation patterns in helical regions of ribosomal RNA (rRNA) genes has the potential to misdirect the estimation of the phylogenetic signal of the data. Furthermore, the extremes of length variation among taxa, combined with regional substitution rate variation can mislead the alignment of rRNA sequences and thus distort subsequent tree reconstructions. However, recent developments in phylogenetic methodology now allow a comprehensive integration of secondary structures in alignment and tree reconstruction analyses based on rRNA sequences, which has been shown to correct some of these problems. Here, we explore the potentials of RNA substitution models and the interactions of specific model setups with the inherent pattern of covariation in rRNA stems and substitution rate variation among loop regions. RESULTS: We found an explicit impact of RNA substitution models on tree reconstruction analyses. The application of specific RNA models in tree reconstructions is hampered by interaction between the appropriate modelling of covarying sites in stem regions, and excessive homoplasy in some loop regions. RNA models often failed to recover reasonable trees when single-stranded regions are excessively homoplastic, because these regions contribute a greater proportion of the data when covarying sites are essentially downweighted. In this context, the RNA6A model outperformed all other models, including the more parametrized RNA7 and RNA16 models. CONCLUSIONS: Our results depict a trade-off between increased accuracy in estimation of interdependencies in helical regions with the risk of magnifying positions lacking phylogenetic signal. We can therefore conclude that caution is warranted when applying rRNA covariation models, and suggest that loop regions be independently screened for phylogenetic signal, and eliminated when they are indistinguishable from random noise. In addition to covariation and homoplasy, other factors, like non-stationarity of substitution rates and base compositional heterogeneity, can disrupt the signal of ribosomal RNA data. All these factors dictate sophisticated estimation of evolutionary pattern in rRNA data, just as other molecular data require similarly complicated (but different) corrections.

The impact of rRNA secondary structure consideration in alignment and tree reconstruction: simulated data and a case study on the phylogeny of hexapods.

The use of secondary structures has been advocated to improve both the alignment and the tree reconstruction processes of ribosomal RNA (rRNA) data sets. We used simulated and empirical rRNA data to test the impact of secondary structure consideration in both steps of molecular phylogenetic analyses. A simulation approach was used to generate realistic rRNA data sets based on real 16S, 18S, and 28S sequences and structures in combination with different branch length and topologies. Alignment and tree reconstruction performance of four recent structural alignment methods was compared with exclusively sequence-based approaches. As empirical data, we used a hexapod rRNA data set to study the influence of nucleotide interdependencies in sequence alignment and tree reconstruction. Structural alignment methods delivered significantly better sequence alignments compared with pure sequence-based methods. Also, structural alignment methods delivered better trees judged by topological congruence to simulation base trees. However, the advantage of structural alignments was less pronounced and even vanished in several instances. For simulated data, application of mixed RNA/DNA models to stems and loops, respectively, led to significantly shorter branches. The application of mixed RNA/DNA models in the hexapod analyses delivered partly implausible relationships. This can be interpreted as a stronger sensitivity of mixed model setups to nonphylogenetic signal. Secondary structure consideration clearly influenced sequence alignment and tree reconstruction of ribosomal genes. Although sequence alignment quality can considerably be improved by the use of secondary structure information, the application of mixed models in tree reconstructions needs further studies to understand the observed effects.

Can comprehensive background knowledge be incorporated into substitution models to improve phylogenetic analyses? A case study on major arthropod relationships.

BACKGROUND: Whenever different data sets arrive at conflicting phylogenetic hypotheses, only testable causal explanations of sources of errors in at least one of the data sets allow us to critically choose among the conflicting hypotheses of relationships. The large (28S) and small (18S) subunit rRNAs are among the most popular markers for studies of deep phylogenies. However, some nodes supported by this data are suspected of being artifacts caused by peculiarities of the evolution of these molecules. Arthropod phylogeny is an especially controversial subject dotted with conflicting hypotheses which are dependent on data set and method of reconstruction. We assume that phylogenetic analyses based on these genes can be improved further i) by enlarging the taxon sample and ii) employing more realistic models of sequence evolution incorporating non-stationary substitution processes and iii) considering covariation and pairing of sites in rRNA-genes. RESULTS: We analyzed a large set of arthropod sequences, applied new tools for quality control of data prior to tree reconstruction, and increased the biological realism of substitution models. Although the split-decomposition network indicated a high noise content in the data set, our measures were able to both improve the analyses and give causal explanations for some incongruities mentioned from analyses of rRNA sequences. However, misleading effects did not completely disappear. CONCLUSION: Analyses of data sets that result in ambiguous phylogenetic hypotheses demand for methods, which do not only filter stochastic noise, but likewise allow to differentiate phylogenetic signal from systematic biases. Such methods can only rely on our findings regarding the evolution of the analyzed data. Analyses on independent data sets then are crucial to test the plausibility of the results. Our approach can easily be extended to genomic data, as well, whereby layers of quality assessment are set up applicable to phylogenetic reconstructions in general.

Simultaneous alignment and folding of 28S rRNA sequences uncovers phylogenetic signal in structure variation.

Secondary structure models of mitochondrial and nuclear (r)RNA sequences are frequently applied to aid the alignment of these molecules in phylogenetic analyses. Additionally, it is often speculated that structure variation of (r)RNA sequences might profitably be used as phylogenetic markers. The benefit of these approaches depends on the reliability of structure models. We used a recently developed approach to show that reliable inference of large (r)RNA secondary structures as a prerequisite of simultaneous sequence and structure alignment is feasible. The approach iteratively establishes local structure constraints of each sequence and infers fully folded individual structures by constrained MFE optimization. A comparison of structure edit distances of individual constraints and fully folded structures showed pronounced phylogenetic signal in fully folded structures. As model sequences we characterized secondary structures of 28S rRNA sequences of selected insects and examined their phylogenetic signal according to established phylogenetic hypotheses.