Proteinprophet fdr biography

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    Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics

    Abstract

    Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in

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    Abstract

    Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the upptäckt of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only bygd combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated bygd tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined

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    A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet

    Statement of the problem from a statistical perspective, and terminology

    Every statistical approach requires the definition of the following components in the problem:

    1. 1.

      PeptideProphet works with the observed spectra as the experimental unit where we have N observed spectra with N being generally large (in the thousands or more). Since the number of spectra N is typically very large, the identified spectra can be viewed as the underlying population.

    2. 2.

      An observed score is interpreted as a test statistic. In statistics the summarized score S is called a test statistic because it is the function of the observed experimental enhet that is being used to answer our hypotheses.

    3. 3.

      PeptideProphet assumes that the test statistic comes from a mixture of two distributions: one from the distribution of correct identifications, and the other from the distribution of the incorre