237 research outputs found

    amsrpm: Robust Point Matching for Retention Time Aligment of LC/MS Data with R

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    Proteomics is the study of the abundance, function and dynamics of all proteins present in a living organism, and mass spectrometry (MS) has become its most important tool due to its unmatched sensitivity, resolution and potential for high-throughput experimentation. A frequently used variant of mass spectrometry is coupled with liquid chromatography (LC) and is denoted as "LC/MS". It produces two-dimensional raw data, where significant distortions along one of the dimensions can occur between different runs on the same instrument, and between instruments. A compensation of these distortions is required to allow for comparisons between and inference based on different experiments. This article introduces the amsrpm software package. It implements a variant of the Robust Point Matching (RPM) algorithm that is tailored for the alignment of LC and LC/MS experiments. Problem-specific enhancements include a specialized dissimilarity measure, and means to enforce smoothness and monotonicity of the estimated transformation function. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. Its usefulness is demonstrated using both simulated and experimental data. The software is available as an open source package for the statistical programming language R.

    NITPICK: peak identification for mass spectrometry data

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    <p>Abstract</p> <p>Background</p> <p>The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.</p> <p>Results</p> <p>This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on <it>fractional averagine</it>, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.</p> <p>Conclusion</p> <p>Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from <url>http://hci.iwr.uni-heidelberg.de/mip/proteomics/</url>.</p

    amsrpm: Robust Point Matching for Retention Time Aligment of LC/MS Data with R

    Get PDF
    Proteomics is the study of the abundance, function and dynamics of all proteins present in a living organism, and mass spectrometry (MS) has become its most important tool due to its unmatched sensitivity, resolution and potential for high-throughput experimentation. A frequently used variant of mass spectrometry is coupled with liquid chromatography (LC) and is denoted as "LC/MS". It produces two-dimensional raw data, where significant distortions along one of the dimensions can occur between different runs on the same instrument, and between instruments. A compensation of these distortions is required to allow for comparisons between and inference based on different experiments. This article introduces the amsrpm software package. It implements a variant of the Robust Point Matching (RPM) algorithm that is tailored for the alignment of LC and LC/MS experiments. Problem-specific enhancements include a specialized dissimilarity measure, and means to enforce smoothness and monotonicity of the estimated transformation function. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. Its usefulness is demonstrated using both simulated and experimental data. The software is available as an open source package for the statistical programming language R

    Characterization of the porcine synovial fluid proteome and a comparison to the plasma proteome

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    AbstractSynovial fluid is present in all joint cavities, and protects the articular cartilage surfaces in large by lubricating the joint, thus reducing friction. Several studies have described changes in the protein composition of synovial fluid in patients with joint disease. However, the protein concentration, content, and synovial fluid volume change dramatically during active joint diseases and inflammation, and the proteome composition of healthy synovial fluid is incompletely characterized.We performed a normative proteomics analysis of porcine synovial fluid, and report data from optimizing proteomic methods to investigate the proteome of healthy porcine synovial fluid (Bennike et al., 2014 [1]). We included an evaluation of different proteolytic sample preparation techniques, and an analysis of posttranslational modifications with a focus on glycosylation. We used pig (Sus Scrofa) as a model organism, as the porcine immune system is highly similar to human and the pig genome is sequenced. Furthermore, porcine model systems are commonly used large animal models to study several human diseases.In addition, we analyzed the proteome of human plasma, and compared the proteomes to the obtained porcine synovial fluid proteome. The proteome of the two body fluids were found highly similar, underlining the detected plasma derived nature of many synovial fluid components. The healthy porcine synovial fluid proteomics data, human rheumatoid arthritis synovial fluid proteomics data used in the method optimization, human plasma proteomics data, and search results, have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier http://www.ebi.ac.uk/pride/archive/projects/PXD000935

    Gas2l3, a Novel Constriction Site-Associated Protein Whose Regulation Is Mediated by the APC/CCdh1APC/C^{Cdh1} Complex

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    Growth arrest-specific 2-like protein 3 (Gas2l3) was recently identified as an Actin/Tubulin cross-linker protein that regulates cytokinesis. Using cell-free systems from both frog eggs and human cells, we show that the Gas2l3 protein is targeted for ubiquitin-mediated proteolysis by the APC/CCdh1APC/C^{Cdh1} complex, but not by the APC/CCdc20APC/C^{Cdc20} complex, and is phosphorylated by Cdk1 in mitosis. Moreover, late in cytokinesis, Gas2l3 is exclusively localized to the constriction sites, which are the narrowest parts of the intercellular bridge connecting the two daughter cells. Overexpression of Gas2l3 specifically interferes with cell abscission, which is the final stage of cell division, when the cutting of the intercellular bridge at the constriction sites occurs. We therefore suggest that Gas2l3 is part of the cellular mechanism that terminates cell division

    Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer's disease biomarkers

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    To develop therapies for Alzheimer's disease, we need accurate in vivo diagnostics. Multiple proteomic studies mapping biomarker candidates in cerebrospinal fluid (CSF) resulted in little overlap. To overcome this shortcoming, we apply the rarely used concept of proteomics meta-analysis to identify an effective biomarker panel. We combine ten independent datasets for biomarker identification: seven datasets from 150 patients/controls for discovery, one dataset with 20 patients/controls for down-selection, and two datasets with 494 patients/controls for validation. The discovery results in 21 biomarker candidates and down-selection in three, to be validated in the two additional large-scale proteomics datasets with 228 diseased and 266 control samples. This resulting 3-protein biomarker panel differentiates Alzheimer's disease (AD) from controls in the two validation cohorts with areas under the receiver operating characteristic curve (AUROCs) of 0.83 and 0.87, respectively. This study highlights the value of systematically re-analyzing previously published proteomics data and the need for more stringent data deposition

    A Snapshot on the Buildup of the Stable Water Isotopic Signal in the Upper Snowpack at EastGRIP on the Greenland Ice Sheet

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    The stable water isotopic composition in firn and ice cores provides valuable information on past climatic conditions. Because of uneven accumulation and post-depositional modifications on local spatial scales up to hundreds of meters, time series derived from adjacent cores differ significantly and do not directly reflect the temporal evolution of the precipitated snow isotopic signal. Hence, a characterization of how the isotopic profile in the snow develops is needed to reliably interpret the isotopic variability in firn and ice cores. By combining digital elevation models of the snow surface and repeated high-resolution snow sampling for stable water isotope measurements of a transect at the East Greenland Ice-core Project campsite on the Greenland Ice Sheet, we are able to visualize the buildup and post-depositional changes of the upper snowpack across one summer season. To this end, 30 cm deep snow profiles were sampled on six dates at 20 adjacent locations along a 40 m transect. Near-daily photogrammetry provided snow height information for the same transect. Our data shows that erosion and redeposition of the original snowfall lead to a complex stratification in the δ18O signature. Post-depositional processes through vapor-snow exchange affect the near surface snow with d-excess showing a decrease in surface and near-surface layers. Our data suggests that the interplay of stratigraphic noise, accumulation intermittency, and local post-depositional processes form the proxy signal in the upper snowpack.publishedVersio
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