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Quickstart

Here we briefly introduce spectrum_utils' spectrum processing and visualization functionality:

  • Load a spectrum from an online data resource by its Universal Spectrum Identifier (USI).
  • Restrict the mass range to 100–1400 m/z to filter out irrelevant peaks.
  • Remove the precursor peak.
  • Remove low-intensity noise peaks by only retaining peaks that are at at least 5% of the base peak intensity and restrict the total number of peaks to the 50 most intense peaks.
  • Scale the peak intensities by their square root to de-emphasize overly intense peaks.
  • Annotate peaks corresponding to a, b, and y peptide fragments in the spectrum based on a ProForma 2.0 peptide string.
  • Visualize the spectrum with the annotated peaks highlighted.

IO functionality to read spectra from MS data files is not directly included in spectrum_utils. Instead you can use excellent libraries to read a variety of mass spectrometry data formats such as Pyteomics or pymzML.

>>> import matplotlib.pyplot as plt
>>> import spectrum_utils.plot as sup
>>> import spectrum_utils.spectrum as sus


# Retrieve the spectrum by its USI.
>>> usi = "mzspec:PXD000561:Adult_Frontalcortex_bRP_Elite_85_f09:scan:17555"
>>> peptide = "VLHPLEGAVVIIFK"
>>> spectrum = sus.MsmsSpectrum.from_usi(usi)

# Process the spectrum.
>>> fragment_tol_mass, fragment_tol_mode = 10, "ppm"
>>> spectrum = (
...    spectrum.set_mz_range(min_mz=100, max_mz=1400)
...    .remove_precursor_peak(fragment_tol_mass, fragment_tol_mode)
...    .filter_intensity(min_intensity=0.05, max_num_peaks=50)
...    .scale_intensity("root")
...    .annotate_proforma(
...        peptide, fragment_tol_mass, fragment_tol_mode, ion_types="aby")
... )

# Plot the spectrum.
>>> fig, ax = plt.subplots(figsize=(12, 6))
>>> _=sup.spectrum(spectrum, grid=False, ax=ax);
>>> ax.spines["right"].set_visible(False)
>>> ax.spines["top"].set_visible(False)
>>> plt.savefig("quickstart.png", bbox_inches="tight", dpi=300, transparent=True)
>>> plt.close()

As demonstrated, each of the processing steps can be achieved using a single, high-level function call. These calls can be chained together to easily perform multiple processing steps.

Spectrum plotting can similarly be achieved using a high-level function call, resulting in the following figure:

Note that several processing steps modify the peak m/z and intensity values and are thus not idempotent. It is recommended to make a copy of the MsmsSpectrum object prior to any processing if the raw peak values need to remain available as well.