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.

We here present the experiences collected maintaining and updating the MoSGrid science gateway over the past years. Insights are provided on a technical and organizational level useful for the design and operation of science gateways in general. The specific challenges faced and solved are considered to be valuable for other communities.

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SUMMARYState‐of‐the‐art research in a variety of natural sciences depends heavily on methods of computational chemistry, for example, the calculation of the properties of materials, proteins, catalysts, and drugs. Applications providing such methods require a lot of expertise to handle their complexity and the usage of high‐performance computing. The MoSGrid (molecular simulation grid) infrastructure relieves this burden from scientists by providing a science gateway, which eases access to and usage of computational chemistry applications. One of its cornerstones is the molecular simulations markup language (MSML), an extension of the chemical markup language. MSML abstracts all chemical as well as computational aspects of simulations. An application and its results can be described with common semantics. Using such application, independent descriptions users can easily switch between different applications or compare them. This paper introduces MSML, its integration into a science gateway, and its usage for molecular dynamics, quantum chemistry, and protein docking. Copyright © 2013 John Wiley & Sons, Ltd.

The MoSGrid (Molecular Simulation Grid) project is currently establishing a platform that aims to be used by both experienced and inexperienced researchers to submit molecular simulation calculations, monitor their progress, and retrieve the results. It provides a webbased portal to easily set up, run, and evaluate molecular simulations carried out on D-Grid resources. The range of applications available encompasses quantum chemistry, molecular dynamics, and protein-ligand docking codes.In addition, data repositories were developed, which contain the results of calculations as well as "recipes" or workflows. These can be used, improved, and distributed by the users. A distributed high-throughput file system allows efficient access to large amounts of data in the repositories. For storing both the input and output of the calculations, we have developed MSML (Molecular Simulation Markup Language), a CML derivative (Chemical Markup Language). MSML has been designed to store structural information on small as well as large molecules and results from various molecular simulation tools and docking tools. It ensures interoperability of different tools through a consistent data representation.At http://www.mosgrid.de the new platform is already available to the scientific community in a beta test phase. Currently, portlets for generic workflows, Gaussian, and Gromacs applications are publicly accessible [1,2].