This vignette describes the new functionality implemented version 2.0 of the ‘synapter’ package. It describes the typical workflow including the 3D grid search, fragment matching and correction of detector saturation.
synapter 2.28.0
synapter is free and open-source software. If you use it, please support the project by citing it in publications:
Nicholas James Bond, Pavel Vyacheslavovich Shliaha, Kathryn S. Lilley, and Laurent Gatto. Improving qualitative and quantitative performance for MS\(^E\)-based label free proteomics. J. Proteome Res., 2013, 12 (6), pp 2340–2353
For bugs, typos, suggestions or other questions, please file an issue
in our tracking system (https://github.com/lgatto/synapter/issues)
providing as much information as possible, a reproducible example and
the output of sessionInfo().
If you don’t have a GitHub account or wish to reach a broader audience for general questions about proteomics analysis using R, you may want to use the Bioconductor support site: https://support.bioconductor.org/.
Here we describe the new functionality implemented in synapter 2.0. Namely this vignette covers the utilisation of the new 3D grid search, the fragment matching, intensity modeling and correction of detector saturation.
The synapter2 workflow is similar to the old one in synapter1.
First it is necessary to use PLGS to create the csv (and xml) files.
Therefore we refer the reader to the default synapter vignette,
available online
and with vignette("synapter", package = "synapter").
In contrast to the original workflow the final_fragment.csv file for the
identification run and a Spectrum.xml file for the quantification run are
needed if the fragment matching should be applied.
Subsequently the original workflow is enhanced by the new
3D grid search and the intensity modeling.
Afterwards the fragment matching could be applied.
MSnbase (Gatto and Lilley 2012) is used for further analysis.
The new synapter adds
synapter/PLGS consensus filtering
and the detector saturation correction for MSnSets.