Ingo Ebersberger

Summary The nuclear ribosomal internal transcribed spacer ( ITS ) region is the primary choice for molecular identification of fungi. Its two highly variable spacers ( ITS 1 and ITS 2) are usually species specific, whereas the intercalary 5.8S gene is highly conserved. For sequence clustering and blast searches, it is often advantageous to rely on either one of the variable spacers but not the conserved 5.8S gene. To identify and extract ITS 1 and ITS 2 from large taxonomic and environmental data sets is, however, often difficult, and many ITS sequences are incorrectly delimited in the public sequence databases. We introduce ITS x, a Perl‐based software tool to extract ITS 1, 5.8S and ITS 2 – as well as full‐length ITS sequences – from both Sanger and high‐throughput sequencing data sets. ITS x uses hidden Markov models computed from large alignments of a total of 20 groups of eukaryotes, including fungi, metazoans and plants, and the sequence extraction is based on the predicted positions of the ribosomal genes in the sequences. ITS x has a very high proportion of true‐positive extractions and a low proportion of false‐positive extractions. Additionally, process parallelization permits expedient analyses of very large data sets, such as a one million sequence amplicon pyrosequencing data set. ITS x is rich in features and written to be easily incorporated into automated sequence analysis pipelines. ITS x paves the way for more sensitive blast searches and sequence clustering operations for the ITS region in eukaryotes. The software also permits elimination of non‐ ITS sequences from any data set. This is particularly useful for amplicon‐based next‐generation sequencing data sets, where insidious non‐target sequences are often found among the target sequences. Such non‐target sequences are difficult to find by other means and would contribute noise to diversity estimates if left in the data set.

Arthropods were the first animals to conquer land and air. They encompass more than three quarters of all described living species. This extraordinary evolutionary success is based on an astoundingly wide array of highly adaptive body organizations. A lack of robustly resolved phylogenetic relationships, however, currently impedes the reliable reconstruction of the underlying evolutionary processes. Here, we show that phylogenomic data can substantially advance our understanding of arthropod evolution and resolve several conflicts among existing hypotheses. We assembled a data set of 233 taxa and 775 genes from which an optimally informative data set of 117 taxa and 129 genes was finally selected using new heuristics and compared with the unreduced data set. We included novel expressed sequence tag (EST) data for 11 species and all published phylogenomic data augmented by recently published EST data on taxonomically important arthropod taxa. This thorough sampling reduces the chance of obtaining spurious results due to stochastic effects of undersampling taxa and genes. Orthology prediction of genes, alignment masking tools, and selection of most informative genes due to a balanced taxa-gene ratio using new heuristics were established. Our optimized data set robustly resolves major arthropod relationships. We received strong support for a sister group relationship of onychophorans and euarthropods and strong support for a close association of tardigrades and cycloneuralia. Within pancrustaceans, our analyses yielded paraphyletic crustaceans and monophyletic hexapods and robustly resolved monophyletic endopterygote insects. However, our analyses also showed for few deep splits that were recently thought to be resolved, for example, the position of myriapods, a remarkable sensitivity to methods of analyses.