Peptide Identification
Database Search with pyOpenMS
from pyopenms import MSExperiment, MzMLFile, FASTAFile, ProteaseDigestion
from pyopenms import ModificationsDB, AASequence
# Load FASTA database
fasta_entries = []
FASTAFile().load('uniprot_human.fasta', fasta_entries)
# In-silico digestion
digestion = ProteaseDigestion()
digestion.setEnzyme('Trypsin')
digestion.setMissedCleavages(2)
peptides = []
for entry in fasta_entries:
seq = AASequence.fromString(entry.sequence)
result = []
digestion.digest(seq, result)
peptides.extend([(entry.identifier, str(p)) for p in result])
Working with Search Results (idXML)
from pyopenms import IdXMLFile, ProteinIdentification, PeptideIdentification
protein_ids = []
peptide_ids = []
IdXMLFile().load('search_results.idXML', protein_ids, peptide_ids)
for pep_id in peptide_ids:
rt = pep_id.getRT()
mz = pep_id.getMZ()
for hit in pep_id.getHits():
sequence = hit.getSequence()
score = hit.getScore()
charge = hit.getCharge()
FDR Estimation (Target-Decoy)
def calculate_fdr(scores, is_decoy, score_threshold):
above_threshold = scores >= score_threshold
n_target = ((~is_decoy) & above_threshold).sum()
n_decoy = (is_decoy & above_threshold).sum()
fdr = n_decoy / n_target if n_target > 0 else 1.0
return fdr
def find_score_at_fdr(scores, is_decoy, target_fdr=0.01):
sorted_scores = np.sort(scores)[::-1]
for threshold in sorted_scores:
fdr = calculate_fdr(scores, is_decoy, threshold)
if fdr <= target_fdr:
return threshold
return sorted_scores[-1]
R: Search Result Processing
library(MSnbase)
# Read mzIdentML results
psms <- readMzIdData('results.mzid')
# Filter to 1% FDR
psms_filtered <- psms[psms$qvalue <= 0.01, ]
# Unique peptides per protein
peptide_counts <- table(psms_filtered$accession)
Spectral Library Search
from pyopenms import SpectraSTSearchAlgorithm, MSExperiment
# Load spectral library
library = MSExperiment()
MzMLFile().load('spectral_library.mzML', library)
# Match query spectra against library
# Returns similarity scores and library matches
Related Skills
- data-import - Load raw MS data before identification
- protein-inference - Group peptides to proteins
- ptm-analysis - Identify modified peptides
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