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Träfflista för sökning "WFRF:(Hoopmann Michael R.) srt2:(2012-2014)"

Sökning: WFRF:(Hoopmann Michael R.) > (2012-2014)

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1.
  • McIlwain, Sean, et al. (författare)
  • Crux : Rapid Open Source Protein Tandem Mass Spectrometry Analysis
  • 2014
  • Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 13:10, s. 4488-4491
  • Tidskriftsartikel (refereegranskat)abstract
    • Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data.
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2.
  • Moruz, Luminita, 1982-, et al. (författare)
  • Mass Fingerprinting of Complex Mixtures : Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
  • 2013
  • Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 12:12, s. 5730-5741
  • Tidskriftsartikel (refereegranskat)abstract
    • In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
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3.
  • Serang, Oliver, et al. (författare)
  • Recognizing Uncertainty Increases Robustness and Reproducibility of Mass Spectrometry-based Protein Inferences
  • 2012
  • Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 11:12, s. 5586-5591
  • Tidskriftsartikel (refereegranskat)abstract
    • Parsimony and protein grouping are widely employed to enforce economy in the number of identified proteins, with the goal of increasing the quality and reliability of protein identifications; however, in a counterintuitive manner, parsimony and protein grouping may actually decrease the reproducibility and interpretability of protein identifications. We present a simple illustration demonstrating ways in which parsimony and protein grouping may lower the reproducibility or interpretability of results. We then provide an example of a data set where a probabilistic method increases the reproducibility and interpretability of identifications made on replicate analyses of Human Du145 prostate cancer cell lines.
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  • Resultat 1-3 av 3

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