42 research outputs found

    The transition of the European Proteomics Association into the future

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    The following report provides an overview of the discussions and outcome of the EuPA General Council meeting that took place in Estoril 20–21 October 2010. During the annual meeting future policy and action plans in a variety of areas are decided. Several important points were decided upon during this meeting including the expansion of the EuPA Executive Committee by introducing a new EuPA committee – EuPA Developments – that will initially spearhead activities in standardisation, imaging ms and biobanking. The EuPA General Council also invited Russia as its 17th member. More details about these and additional activities are presented in the article

    Development and promotion in translational medicine: perspectives from 2012 sino-american symposium on clinical and translational medicine

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    Background Clinical translational medicine (CTM) is an emerging area comprising multidisciplinary research from basic science to medical applications and entails a close collaboration among hospital, academia and industry. Findings This Session focused discussing on new models for project development and promotion in translational medicine. The conference stimulated the scientific and commercial communication of project development between academies and companies, shared the advanced knowledge and expertise of clinical applications, and created the environment for collaborations. Conclusions Although strategic collaborations between corporate and academic institutions have resulted in a state of resurgence in the market, new cooperation models still need time to tell whether they will improve the translational medicine process

    Integrated proteogenomic approach identifying a protein signature of COPD and a new splice variant of SORBS1

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    Translation of genomic alterations to protein changes in chronic obstructive pulmonary disease (COPD) is largely unexplored. Using integrated proteomic and RNA sequencing analysis of COPD and control lung tissues, we identified a protein signature in COPD characterised by extracellular matrix changes and a potential regulatory role for SUMO2. Furthermore, we identified 61 differentially expressed novel, non-reference, peptides in COPD compared with control lungs. This included two peptides encoding for a new splice variant of SORBS1, of which the transcript usage was higher in COPD compared with control lungs. These explorative findings and integrative proteogenomic approach open new avenues to further unravel the pathology of COPD

    The Quest for High-Speed and Low-Volume Bioanalysis

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    Linking Proteins to Disease Processes

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    Correlation Queries for Mass Spectrometry Imaging

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    <p>Mass spectrometry imaging (MSI) generates large volumetric data sets consisting of mass to charge ratio (m/z), ion current, and x,y coordinate location. These data sets usually serve limited purposes centered on measuring the distribution of a small set of ions with known m/z. Such earmarked queries consider only a fraction of the full mass spectrum captured, and there are few tools to assist the exploration of the remaining volume of unknown data in terms of demonstrating similarity or discordance in tissue compartment distribution patterns. Here we present a novel, interactive approach to extract information from MSI data that relies on precalculated data structures to perform queries of large data sets with a typical laptop. We have devised methods to query the full volume to find new m/z values of potential interest based on similarity to biological structures or to the spatial distribution of known ions. We describe these query methods in detail and provide examples demonstrating the power of the methods to "discover" m/z values of ions that have such potentially interesting correlations. The "discovered" ions may be further correlated with either positional locations or the coincident distribution of other ions using successive queries. Finally, we show it is possible to gain insight to the fragmentation pattern of the parent molecule from such correlations. The ability to discover new ions of interest in the unknown bulk of an MSI data set offers the potential to further our understanding of biological and physiological processes related to health and disease.</p>

    Clusterwise Peak Detection and Filtering Based on Spatial Distribution to Efficiently Mine Mass Spectrometry Imaging Data

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    Mass spectrometry imaging (MSI) has the potential to reveal the localization of thousands of biomolecules such as metabolites and lipids in tissue sections. The increase in both mass and spatial resolution of today's instruments brings on considerable challenges in terms of data processing; accurately extracting meaningful signals from the large data sets generated by MSI without losing information that could be clinically relevant is one of the most fundamental tasks of analysis software. Ion images of the biomolecules are generated by visualizing their intensities in 2-D space using mass spectra collected across the tissue section. The intensities are often calculated by summing each compound's signal between predefined sets of borders (bins) in the m/z dimension. This approach, however, can result in mixed signals from different compounds in the same bin or splitting the signal from one compound between two adjacent bins, leading to low quality ion images. To remedy this problem, we propose a novel data processing approach. Our approach consists of a sensitive peak detection method able to discover both faint and localized signals by utilizing clusterwise kernel density estimates (KDEs) of peak distributions. We show that our method can recall more ground-truth molecules, molecule fragments, and isotopes than existing methods based on binning. Furthermore, it automatically detects previously reported molecular ions of lipids, including those close in m/z, in an experimental data set
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