This vignette describes how to apply fragment matching to MS\(^E\)/HDMS\(^E\) data using the ‘synapter’ package. The fragment matching feature allows one to rescue non unique matches and remove falsely assigned unique matches as well.
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
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/.
This document assumes familiarity with standard synapter pipeline described in (Bond et al. 2013) and in the package synapter vignette, available online and with
vignette("synapter", package = "synapter").
In this vignette we introduce a new fragment matching feature (see figures 2, 3 and 4) which improves the matching of identification and the quantitation features. After applying the usual
synergise1 workflow (see
?Synapter for details) a number of multiple matches and possible false unique matches remain that can be deconvoluted by comparing common peaks in the identification fragment peaks and the quantitation spectra.
The example data
synobj2 used throughout this document is available in the synapterdata package and can be directly load as follows:
In the [next section](:synergise] we describe how
synobj2 was generated. The following sections then describe the new fragment matching functionality.
One has to run the
synergise1 workflow before fragment matching can be applied. Please read the general synapter vignette for the general use of
synergise1. The additional data needed for the fragment matching procedure are a
final_fragment.csv file for the identification run and a
Spectrum.xml file for the quantitation run.
## Please find the raw data at: ## http://proteome.sysbiol.cam.ac.uk/lgatto/synapter/data/ library("synapter") inlist <- list( identpeptide = "fermentor_03_sample_01_HDMSE_01_IA_final_peptide.csv.gz", identfragments = "fermentor_03_sample_01_HDMSE_01_IA_final_fragment.csv.gz", quantpeptide = "fermentor_02_sample_01_HDMSE_01_IA_final_peptide.csv.gz", quantpep3d = "fermentor_02_sample_01_HDMSE_01_Pep3DAMRT.csv.gz", quantspectra = "fermentor_02_sample_01_HDMSE_01_Pep3D_Spectrum.xml.gz", fasta = "S.cerevisiae_Uniprot_reference_canonical_18_03_14.fasta") synobj2 <- Synapter(inlist, master=FALSE)
synobj2 <- synergise1(object=synobj2, outputdir=tempdir())
This step is optional and allows one to remove low abundance fragments in the spectra using
filterFragments. Filtering fragments can remove noise in the spectra and reduce undesired fragment matches. Prior to filtering, the
plotCumulativeNumberOfFragments function can be use to visualise the intensity of all fragments. Both functions have an argument
what to decide what spectra/fragments to filter/plot. Choose
fragments.ident for the identification fragments and
spectra.quant for the quantitation fragments.
plotCumulativeNumberOfFragments(synobj2, what = "fragments.ident")
plotCumulativeNumberOfFragments(synobj2, what = "spectra.quant")
filterFragments(synobj2, what = "fragments.ident", minIntensity = 70) filterFragments(synobj2, what = "spectra.quant", minIntensity = 70)
This method, named
fragmentMatching, performs the matching of the identification fragments vs the quantitation spectra and counts the number of identical peaks for each combination.
Because the peaks/fragments in the spectra of one run will never be numerically identical to these in another, a tolerance parameter has to be set using the
setFragmentMatchingPpmTolerance function. Peaks/Fragments within this tolerance are treated as identical.
setFragmentMatchingPpmTolerance(synobj2, 25) fragmentMatching(synobj2)
plotFragmentMatching function illustrates the details of this fragment matching procedure. If it is called without any additional argument every matched pair (fragment vs spectrum) is plotted. One can use the
key argument to select a special value in a column (defined by the
column argument) of the
data.frame. E.g. if one wants to select the fragment matching results with a high number of common peaks, e.g. 28 common peaks:
plotFragmentMatching(synobj2, key = 28, column = "FragmentMatching")