Pladipus enables universal distributed computing in proteomics bioinformatics

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open -source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries

MAPPI-DAT : data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments

The Online Protein Processing Resource (TOPPR) : a database and analysis platform for protein processing events

We here present The Online Protein Processing Resource (TOPPR; http://iomics.ugent.be/toppr/), an online database that contains thousands of published proteolytically processed sites in human and mouse proteins. These cleavage events were identified with COmbinded FRActional DIagonal Chromatography proteomics technologies, and the resulting database is provided with full data provenance. Indeed, TOPPR provides an interactive visual display of the actual fragmentation mass spectrum that led to each identification of a reported processed site, complete with fragment ion annotations and search engine scores. Apart from warehousing and disseminating these data in an intuitive manner, TOPPR also provides an online analysis platform, including methods to analyze protease specificity and substrate-centric analyses. Concretely, TOPPR supports three ways to retrieve data: (i) the retrieval of all substrates for one or more cellular stimuli or assays; (ii) a substrate search by UniProtKB/Swiss-Prot accession number, entry name or description; and (iii) a motif search that retrieves substrates matching a user-defined protease specificity profile. The analysis of the substrates is supported through the presence of a variety of annotations, including predicted secondary structure, known domains and experimentally obtained 3D structure where available. Across substrates, substrate orthologs and conserved sequence stretches can also be shown, with iceLogo visualization provided for the latter

Up-to-date workflow for plant (phospho)proteomics identifies differential drought-responsive phosphorylation events in maize leaves

Protein phosphorylation is one of the most common post-translational modifications (PTMs), which can regulate protein activity and localization as well as proteinprotein interactions in numerous cellular processes. Phosphopeptide enrichment techniques enable plant researchers to acquire insight into phosphorylation-controlled signaling networks in various plant species. Most phosphoproteome analyses of plant samples still involve stable isotope labeling, peptide fractionation, and demand a lot of mass spectrometry (MS) time. Here, we present a simple workflow to probe, map, and catalogue plant phosphoproteomes, requiring relatively low amounts of starting material, no labeling, no fractionation, and no excessive analysis time. Following optimization of the different experimental steps on Arabidopsis thaliana samples, we transferred our workflow to maize, a major monocot crop, to study signaling upon drought stress. In addition, we included normalization to protein abundance to identify true phosphorylation changes. Overall, we identified a set of new phosphosites in both Arabidopsis thaliana and maize, some of which are differentially phosphorylated upon drought. All data are available via ProteomeXchange with identifier PXD003634, but to provide easy access to our model plant and crop data sets, we created an online database, Plant PTM Viewer (bioinformatics.psb.ugent.be/webtools/ptm_viewer/), where all phosphosites identified in our study can be consulted

A Decoy-Free Approach to the Identification of Peptides

A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the “decoy” database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs). Nokoi was trained on a very large collection of heterogeneous data using ranks supplied by the Mascot search engine to label correct and incorrect PSMs. We show that Nokoi outperforms Mascot and achieves a performance very close to that of Percolator at substantially higher processing speeds

Annotated Spectra for Vu, Stes et al

To generate the annotated spectra, we used the following workflow: to parse just the msms scan results and the peak list files we used a modified in house max quant parser (https://github.com/compomics/colims/tree/master/colims-distributed/). We then combined the separate results and extracted the relevant msms ids. Subsequently, all relevant data were presented as a PDF composed out of jfreechart (http://www.jfree.org/) charts