Notebook

Chromatographic Analysis¶

In targeted proteomics, such as SRM / MRM / PRM / DIA applications, groups of chromatograms need to be analyzed frequently. OpenMS provides several powerful tools for analysis of chromatograms. Most of them are part of the OpenSWATH suite of tools and are also discussed in the OpenSwath documentation.

Peak Detection¶

Here, we will focus on a simple example where two peptides are analyzed. We will need 2 input files: the chromatogram files that contains the chromatographic raw data (raw SRM traces or extracted ion chromatograms from PRM/DIA data) as well as the library file used to generated the data which contains information about the targeted peptides:

In [ ]:

from urllib.request import urlretrieve
from pyopenms import *

gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
urlretrieve(gh + "/src/data/OpenSwathAnalyzer_1_input_chrom.mzML", "chrom.mzML")
urlretrieve(
    gh + "/src/data/OpenSwathAnalyzer_1_input.TraML", "transitions.TraML"
)

chroms = MSExperiment()
library = TargetedExperiment()
MzMLFile().load("chrom.mzML", chroms)
TraMLFile().load("transitions.TraML", library)

# Investigate library
for t in library.getTransitions():
    print(
        "Transition",
        t.getNativeID(),
        "belongs to peptide group",
        t.getPeptideRef(),
    )

print(
    "Input contains",
    len(library.getTransitions()),
    "transitions and",
    len(chroms.getChromatograms()),
    "chromatograms.",
)
features = FeatureMap()
dummy_trafo = TransformationDescription()
dummy_exp = MSExperiment()
MRMFeatureFinderScoring().pickExperiment(
    chroms, features, library, dummy_trafo, dummy_exp
)
for f in features:
    print(
        "Feature for group",
        f.getMetaValue("PeptideRef"),
        "with precursor m/z",
        f.getMetaValue("PrecursorMZ"),
    )
    print(
        "  Feature found at RT =",
        f.getRT(),
        "with library dot product",
        f.getMetaValue("var_library_dotprod"),
    )

Here we see that for the first group of transitions (tr_gr1), a single peak at retention time $3119\ seconds$ was found. However, for the second group of transitions, two peaks are found at retention times $3119\ seconds$ and at $3055\ seconds$.

Visualization¶

We can confirm the above analysis by visual inspection of the chrom.mzML file produced above in the TOPPView software:

However, our output above contains more information than only retention time:

Feature for group tr_gr1 with precursor m/z 500.0
  Feature found at RT = 3119.091968219877 with library dot product 0.9924204062692046
Feature for group tr_gr2 with precursor m/z 501.0
  Feature found at RT = 3055.584481870532 with library dot product 0.952054383474221
Feature for group tr_gr2 with precursor m/z 501.0
  Feature found at RT = 3119.0630105310684 with library dot product 0.7501676755451506

Based on the output above, we can infer that the peak at $3055\ seconds$ is likely the correct peak for tr_gr2 since it has a high library dot product ($0.95$) while the peak at $3119\ seconds$ is likely incorrect for tr_gr2 since its dot product is low ($0.75$). We also see that a peak at $3119\ seconds$ is likely correct for tr_gr1 since it matches well with the expected library intensities and has a high dot product ($0.99$).

Note: to get an overview over all available scores for a particular MRM features f, you can use

In [ ]:

k = []
f.getKeys(k)
print(k)

Smoothing¶

Now you may want to show the chromatograms to your collaborator, but you notice that most software solutions smooth the chromatograms before display. In order to provide smooth chromatograms, you can apply a filter using pyOpenMS:

In [ ]:

sg = SavitzkyGolayFilter()
sg.filterExperiment(chroms)
# MzMLFile().store("chrom.filter.mzML", chroms)

Which leads to the following smoothed chromatographic traces: