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
git_url: https://git.bioconductor.org/packages/TargetDecoy
git_branch: RELEASE_3_22
git_last_commit: 612ab7d
git_last_commit_date: 2025-10-29
Repository: Bioconductor 3.22
Date/Publication: 2025-10-29
NeedsCompilation: no
Packaged: 2025-10-30 04:23:37 UTC; biocbuild
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 4.5.1; ; 2025-10-30 09:43:21 UTC; unix
