Identification Data

In OpenMS, identifications of peptides, proteins and small molecules are stored in dedicated data structures. These data structures are typically stored to disc as idXML or mzIdentML file. The highest-level structure is ProteinIdentification. It stores all identified proteins of an identification run as ProteinHit objects plus additional metadata (search parameters, etc.). Each ProteinHit contains the actual protein accession, an associated score, and (optionally) the protein sequence.

A PeptideIdentification object stores the data corresponding to a single identified spectrum or feature. It has members for the retention time, m/z, and a vector of PeptideHit objects. Each PeptideHit stores the information of a specific peptide-to-spectrum match or PSM (e.g., the score and the peptide sequence). Each PeptideHit also contains a vector of PeptideEvidence objects which store the reference to one or more (in the case the peptide maps to multiple proteins) proteins and the position therein.

ProteinIdentification

We can create an object of type ProteinIdentification and populate it with ProteinHit objects as follows:

from pyopenms import *

# Create new protein identification object corresponding to a single search
protein_id = ProteinIdentification()
protein_id.setIdentifier("IdentificationRun1")

# Each ProteinIdentification object stores a vector of protein hits
protein_hit = ProteinHit()
protein_hit.setAccession("sp|MyAccession")
protein_hit.setSequence("PEPTIDERDLQMTQSPSSLSVSVGDRPEPTIDE")
protein_hit.setScore(1.0)
protein_hit.setMetaValue("target_decoy", b"target") # its a target protein

protein_id.setHits([protein_hit])

We have now added a single ProteinHit with the accession sp|MyAccession to the ProteinIdentification object (note how on line 14 we directly added a list of size 1). We can continue to add meta-data for the whole identification run (such as search parameters):

now = DateTime.now()
date_string = now.getDate()
protein_id.setDateTime(now)

# Example of possible search parameters
search_parameters = SearchParameters() # ProteinIdentification::SearchParameters
search_parameters.db = "database"
search_parameters.charges = "+2"
protein_id.setSearchParameters(search_parameters)

# Some search engine meta data
protein_id.setSearchEngineVersion("v1.0.0")
protein_id.setSearchEngine("SearchEngine")
protein_id.setScoreType("HyperScore")

# Iterate over all protein hits
for hit in protein_id.getHits():
  print("Protein hit accession:", hit.getAccession())
  print("Protein hit sequence:", hit.getSequence())
  print("Protein hit score:", hit.getScore())

PeptideIdentification

Next, we can also create a PeptideIdentification object and add corresponding PeptideHit objects:

peptide_id = PeptideIdentification()

peptide_id.setRT(1243.56)
peptide_id.setMZ(440.0)
peptide_id.setScoreType("ScoreType")
peptide_id.setHigherScoreBetter(False)
peptide_id.setIdentifier("IdentificationRun1")

# define additional meta value for the peptide identification
peptide_id.setMetaValue("AdditionalMetaValue", "Value")

# create a new PeptideHit (best PSM, best score)
peptide_hit = PeptideHit()
peptide_hit.setScore(1.0)
peptide_hit.setRank(1)
peptide_hit.setCharge(2)
peptide_hit.setSequence(AASequence.fromString("DLQM(Oxidation)TQSPSSLSVSVGDR"))

ev = PeptideEvidence()
ev.setProteinAccession("sp|MyAccession")
ev.setAABefore(b"R")
ev.setAAAfter(b"P")
peptide_hit.setPeptideEvidences([ev])

# create a new PeptideHit (second best PSM, lower score)
peptide_hit2 = PeptideHit()
peptide_hit2.setScore(0.5)
peptide_hit2.setRank(2)
peptide_hit2.setCharge(2)
peptide_hit2.setSequence(AASequence.fromString("QDLMTQSPSSLSVSVGDR"))
peptide_hit2.setPeptideEvidences([ev])

# add PeptideHit to PeptideIdentification
peptide_id.setHits([peptide_hit, peptide_hit2])

This allows us to represent single spectra (PeptideIdentification at m/z 440.0 and rt 1234.56) with possible identifications that are ranked by score. In this case, apparently two possible peptides match the spectrum which have the first three amino acids in a different order “DLQ” vs “QDL”).

We can now display the peptides we just stored:

# Iterate over PeptideIdentification
peptide_ids = [peptide_id]
for peptide_id in peptide_ids:
  # Peptide identification values
  print ("Peptide ID m/z:", peptide_id.getMZ())
  print ("Peptide ID rt:", peptide_id.getRT())
  print ("Peptide ID score type:", peptide_id.getScoreType())
  # PeptideHits
  for hit in peptide_id.getHits():
    print(" - Peptide hit rank:", hit.getRank())
    print(" - Peptide hit sequence:", hit.getSequence())
    print(" - Peptide hit score:", hit.getScore())
    print(" - Mapping to proteins:", [ev.getProteinAccession()
                                        for ev in hit.getPeptideEvidences() ] )

Storage on disk

Finally, we can store the peptide and protein identification data in a idXML file (a OpenMS internal file format which we have previously discussed here) which we would do as follows:

# Store the identification data in an idXML file
IdXMLFile().store("out.idXML", [protein_id], peptide_ids)
# and load it back into memory
prot_ids = []; pep_ids = []
IdXMLFile().load("out.idXML", prot_ids, pep_ids)

# Iterate over all protein hits
for protein_id in prot_ids:
  for hit in protein_id.getHits():
    print("Protein hit accession:", hit.getAccession())
    print("Protein hit sequence:", hit.getSequence())
    print("Protein hit score:", hit.getScore())
    print("Protein hit target/decoy:", hit.getMetaValue("target_decoy"))

# Iterate over PeptideIdentification
for peptide_id in pep_ids:
  # Peptide identification values
  print ("Peptide ID m/z:", peptide_id.getMZ())
  print ("Peptide ID rt:", peptide_id.getRT())
  print ("Peptide ID score type:", peptide_id.getScoreType())
  # PeptideHits
  for hit in peptide_id.getHits():
    print(" - Peptide hit rank:", hit.getRank())
    print(" - Peptide hit sequence:", hit.getSequence())
    print(" - Peptide hit score:", hit.getScore())
    print(" - Mapping to proteins:", [ev.getProteinAccession() for ev in hit.getPeptideEvidences() ] )

You can inspect the out.idXML XML file produced here, and you will find a <ProteinHit> entry for the protein that we stored and two <PeptideHit> entries for the two peptides stored on disk.