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The in silico prediction of the best-observable “proteotypic” peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Biological significance Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms.

For the evaluation of data from stimulus response experiments dynamic metabolic network models are generated. With an increase of reaction steps and regulatory interdependencies the amount of the simulation data becomes hard to handle. In this paper, we present the application and extension of methods combining visualization and animation of dynamic models to facilitate the analysis of the complex system behaviour. The dynamic changes of metabolite pools and fluxes are simultaneous visualized within the network structure. Depending on the scaling used, different focuses can be set, e.g. to observe local dynamics or global concentration balances. For the visualization of the present inhibition and activation state of certain reaction steps of a metabolic network model a novel quantification method is proposed. The sensitivity analysis of dynamic metabolic network models leads to high-dimensional sensitivity matrices that vary over time. To process the enormous amount of data we use a colour scale transformation and the reorderable matrix method for the visual exploration of the time-varying matrices. The benefits of our methods are illustrated with the help of a metabolic network model of the central carbon metabolism in Escherichia coli.