MotivationLaboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of >1600 proteins using heavy labeled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. ResultsThe web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to >200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available on mousequapro.proteincentre.com. AVAILABILITY AND IMPLEMENTATION: The knowledgebase is available free of charge on http://mousequapro.proteincentre.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High throughput -omics like proteomics and genomics allow detailed molecular studies of organisms. Such studies are inherently on the Big Data side regarding volume and complexity. Following the FAIR principles and reaching for transparency in publication, raw data and results are often shared in public repositories. However, despite the steadily increased amount of shared omics data, it is still challenging to compare, correlate, and integrate it to answer new questions. Here we report on our experience in reusing and repurposing publically available proteomics and genomics data to design new targeted proteomics experiments. We have developed a scientific workflow to retrieve and integrate information from various repositories and domain knowledge-bases including UniPortKB [1], GPMDB [2], PRIDE [3], PeptideAtlas [4], ProteomicsDB [5], MassIVE [6], ExPASy [7], NCBI's dbSNP [8], and PeptideTracker [9]. Following a “Map-Reduce” approach [10] the workflow select best proteotypic peptides for Multiple Reaction Monitoring (MRM) experiment. In an attempt to gain insights into the human proteome, we have designed a second workflow to orchestrate the selection workflow. 100,000s of queries were sent to online repositories to determine if peptides were seen in previous experiments. Fault tolerance ranged from dealing with no-reply to wrong annotations. Three months run of the workflow generated a comprehensive list of 165k+ suitable proteotypic peptides covering most human proteins. The main challenge has been the evolving APIs of the resources which continuously affects the components of our integrative bioinformatic solutions.

In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.

Motivation: Multiple Reaction Monitoring (MRM)-based targeted proteomics is increasingly being used to study the molecular basis of disease. When combined with an internal standard, MRM allows absolute quantification of proteins in virtually any type of sample but the development and validation of an MRM assay for a specific protein is laborious. Therefore, several public repositories now host targeted proteomics MRM assays, including NCI's Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb and PeptideTracker, with all of which contain different levels of information. Results: Here we present MRMAssayDB, a web-based application that integrates these repositories into a single resource. MRMAssayDB maps and links the targeted assays, annotates the proteins with information from UniProtKB, KEGG pathways and Gene Ontologies, and provides several visualization options on the peptide and protein level. Currently MRMAssayDB contains >168K assays covering more than 34K proteins from 63 organisms; >13.5K of these proteins are present in >2.3K KEGG biological pathways corresponding to >300 master pathways, and mapping to >13K GO biological processes. MRMAssayDB allows comprehensive searches for a targeted-proteomics assay depending on the user's interests, by using target-protein name or accession number, or using annotations such as subcellular localization, biological pathway, or disease or drug associations. The user can see how many data repositories include a specific peptide assay, and the commonly used transitions for each peptide in all empirical data from the repositories. Availability and implementation: http://mrmassaydb.proteincentre.com. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal. RESULTS: We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction. CONCLUSION: Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.

Parameter sweeps are used by researchers with scientific domain-specific tools or workflows to submit a large collection of computational jobs whereby each single job of it only varies in certain parts. They require a more fine-grained distribution of jobs across resources, which also raise a significant challenge for efficient resource management in middleware environments that have been not specifically designed to perform parameter sweeps. This paper offers insights into parameter sweep solutions that support multi-disciplinary science environments via abstraction from resource management complexities using middleware. The solutions are based on use case requirements, enable efficient submission, enhanced usability, and standard compliance. We also apply a use case taken from the life science domain to demonstrate usefulness and efficiency of the solutions.

Data protection is a challenge when outsourcing medical analysis, especially if one is dealing with patient related data. While securing transfer channels is possible using encryption mechanisms, protecting the data during analyses is difficult as it usually involves processing steps on the plain data. A common use case in bioinformatics is when a scientist searches for a biological sequence of amino acids or DNA nucleotides in a library or database of sequences to identify similarities. Most such search algorithms are optimized for speed with less or no consideration for data protection. Fast algorithms are especially necessary because of the immense search space represented for instance by the genome or proteome of complex organisms. We propose a new secure exact term search algorithm based on Bloom filters. Our algorithm retains data privacy by using Obfuscated Bloom filters while maintaining the performance needed for real-life applications. The results can then be further aggregated using Homomorphic Cryptography to allow exact-match searching. The proposed system facilitates outsourcing exact term search of sensitive data to on-demand resources in a way which conforms to best practice of data protection.