Package: TargetDecoy
Title: Diagnostic Plots to Evaluate the Target Decoy Approach
Version: 1.16.0
Date: 2022-10-21
Authors@R: c(
    person(given = "Elke",
           family = "Debrie",
           role = c("aut", "cre"),
           email = "elkedebrie@gmail.com"),
    person(given = "Lieven",
           family = "Clement",
           role = c("aut"),
           email = "lieven.clement@ugent.be",
           comment = c(ORCID = "0000-0002-9050-4370")),
    person(given = "Milan",
           family = "Malfait",
           role = "aut",
           email = "milan.malfait@ugent.be",
           comment = c(ORCID = "0000-0001-9144-3701"))
    )
Description: A first step in the data analysis of Mass Spectrometry
        (MS) based proteomics data is to identify peptides and
        proteins. With this respect the huge number of experimental
        mass spectra typically have to be assigned to theoretical
        peptides derived from a sequence database. Search engines are
        used for this purpose. These tools compare each of the observed
        spectra to all candidate theoretical spectra derived from the
        sequence data base and calculate a score for each comparison.
        The observed spectrum is then assigned to the theoretical
        peptide with the best score, which is also referred to as the
        peptide to spectrum match (PSM). It is of course crucial for
        the downstream analysis to evaluate the quality of these
        matches. Therefore False Discovery Rate (FDR) control is used
        to return a reliable list PSMs. The FDR, however, requires a
        good characterisation of the score distribution of PSMs that
        are matched to the wrong peptide (bad target hits). In
        proteomics, the target decoy approach (TDA) is typically used
        for this purpose. The TDA method matches the spectra to a
        database of real (targets) and nonsense peptides (decoys). A
        popular approach to generate these decoys is to reverse the
        target database. Hence, all the PSMs that match to a decoy are
        known to be bad hits and the distribution of their scores are
        used to estimate the distribution of the bad scoring target
        PSMs. A crucial assumption of the TDA is that the decoy PSM
        hits have similar properties as bad target hits so that the
        decoy PSM scores are a good simulation of the target PSM
        scores. Users, however, typically do not evaluate these
        assumptions. To this end we developed TargetDecoy to generate
        diagnostic plots to evaluate the quality of the target decoy
        method.
License: Artistic-2.0
URL: https://www.bioconductor.org/packages/TargetDecoy,
        https://statomics.github.io/TargetDecoy/,
        https://github.com/statOmics/TargetDecoy/
BugReports: https://github.com/statOmics/TargetDecoy/issues
biocViews: MassSpectrometry, Proteomics, QualityControl, Software,
        Visualization
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
Depends: R (>= 4.1)
Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats
Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra,
        testthat (>= 3.0.0), covr
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/pak/sysreqs: cmake make libicu-dev libxml2-dev libnetcdf-dev
        libssl-dev zlib1g-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2025-10-29 15:13:35 UTC
RemoteUrl: https://github.com/bioc/TargetDecoy
RemoteRef: RELEASE_3_22
RemoteSha: 612ab7dd0e32d430cb5bb8746f109714a90f257d
NeedsCompilation: no
Packaged: 2026-01-23 07:11:59 UTC; root
Author: Elke Debrie [aut, cre],
  Lieven Clement [aut] (ORCID: <https://orcid.org/0000-0002-9050-4370>),
  Milan Malfait [aut] (ORCID: <https://orcid.org/0000-0001-9144-3701>)
Maintainer: Elke Debrie <elkedebrie@gmail.com>
Built: R 4.5.2; ; 2026-01-23 07:13:52 UTC; windows
