TargetDecoy

DOI: 10.18129/B9.bioc.TargetDecoy    

Diagnostic Plots to Evaluate the Target Decoy Approach

Bioconductor version: Release (3.14)

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.

Author: Elke Debrie [aut, cre], Lieven Clement [aut] , Milan Malfait [aut]

Maintainer: Elke Debrie <elkedebrie at gmail.com>

Citation (from within R, enter citation("TargetDecoy")):

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("TargetDecoy")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("TargetDecoy")

 

HTML R Script Introduction to TargetDecoy
PDF   Reference Manual

Details

biocViews MassSpectrometry, Proteomics, QualityControl, Software, Visualization
Version 1.0.0
In Bioconductor since BioC 3.14 (R-4.1) (< 6 months)
License Artistic-2.0
Depends R (>= 4.1)
Imports ggplot2, ggpubr, methods, mzID, mzR, stats
LinkingTo
Suggests BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr
SystemRequirements
Enhances
URL https://github.com/statOmics/TargetDecoy
BugReports https://github.com/statOmics/TargetDecoy/issues
Depends On Me
Imports Me
Suggests Me
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Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package TargetDecoy_1.0.0.tar.gz
Windows Binary TargetDecoy_1.0.0.zip
macOS 10.13 (High Sierra) TargetDecoy_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/TargetDecoy
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/TargetDecoy
Package Short Url https://bioconductor.org/packages/TargetDecoy/
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Old Source Packages for BioC 3.14 Source Archive

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