Spectral Library Management
Build Library from DDA Data
SpectraST (TPP)
# Build library from search results
spectrast -cNlibrary.splib -cAC search_results.pep.xml
# Filter library for quality
spectrast -cNfiltered.splib -cAQ library.splib
# Convert to other formats
spectrast -cNlibrary.tsv -cM library.splib
EasyPQP (Skyline/OpenMS)
# Build library from search results
easypqp library \
--in psm_results.tsv \
--out library.pqp \
--psmtsv \
--rt_reference irt.tsv
# Convert to TSV format
easypqp convert \
--in library.pqp \
--out library.tsv \
--format openswath
EncyclopeDIA (Walnut)
# Build chromatogram library from DIA
EncyclopeDIA \
-i sample1.mzML \
-i sample2.mzML \
-l wide_window_library.dlib \
-f uniprot.fasta \
-o results
# Search with narrow-window DIA
EncyclopeDIA \
-i narrow_sample.mzML \
-l narrow_library.elib \
-f uniprot.fasta \
-o search_results
Predicted Libraries
Prosit (Deep Learning)
# Generate predictions via Prosit API
import requests
import pandas as pd
peptides = pd.DataFrame({
'modified_sequence': ['PEPTIDEK', 'ANOTHERPEPTIDER'],
'collision_energy': [30, 30],
'precursor_charge': [2, 2]
})
# Submit to Prosit server
response = requests.post(
'https://www.proteomicsdb.org/prosit/api/predict',
json=peptides.to_dict(orient='records')
)
# Parse response to library format
predictions = response.json()
DeepLC Retention Time Prediction
from deeplc import DeepLC
# Initialize predictor
dlc = DeepLC()
# Predict retention times
peptides = ['PEPTIDEK', 'ANOTHERPEPTIDER']
calibration_peptides = ['GAGSSEPVTGLDAK', 'VEATFGVDESNAK']
calibration_rts = [22.4, 33.1]
# Calibrate and predict
dlc.calibrate_preds(
seq_df=pd.DataFrame({'seq': calibration_peptides, 'rt': calibration_rts})
)
predicted_rts = dlc.make_preds(seq_df=pd.DataFrame({'seq': peptides}))
MS2PIP Fragmentation Prediction
from ms2pip import Predictor
# Initialize predictor
predictor = Predictor(model='HCD2021')
# Predict fragmentation
peptide_df = pd.DataFrame({
'peptide': ['PEPTIDEK', 'ANOTHERPEPTIDER'],
'charge': [2, 2],
'modifications': ['', '']
})
predictions = predictor.predict(peptide_df)
Library Formats
DIA-NN TSV Format
# Required columns
PrecursorMz ProductMz Annotation ProteinId GeneName
PeptideSequence ModifiedSequence PrecursorCharge
FragmentCharge FragmentType FragmentSeriesNumber
NormalizedRetentionTime LibraryIntensity
OpenSWATH TSV Format
import pandas as pd
# Convert to OpenSWATH format
library = pd.DataFrame({
'PrecursorMz': precursor_mz,
'ProductMz': product_mz,
'LibraryIntensity': intensity,
'NormalizedRetentionTime': rt,
'PrecursorCharge': charge,
'ProductCharge': 1,
'FragmentType': ion_type, # 'b' or 'y'
'FragmentSeriesNumber': ion_num,
'ModifiedPeptideSequence': mod_seq,
'PeptideSequence': sequence,
'ProteinId': protein,
'GeneName': gene,
'Decoy': 0
})
library.to_csv('library_openswath.tsv', sep='\t', index=False)
Spectronaut Library Format
# Key columns for Spectronaut
ModifiedPeptide StrippedPeptide PrecursorCharge
PrecursorMz iRT FragmentLossType
FragmentCharge FragmentType FragmentNumber
RelativeIntensity FragmentMz ProteinGroups
Genes ProteinIds
Library QC
import pandas as pd
library = pd.read_csv('library.tsv', sep='\t')
# Basic statistics
print(f"Precursors: {library['ModifiedSequence'].nunique()}")
print(f"Proteins: {library['ProteinId'].nunique()}")
print(f"Transitions per precursor: {len(library) / library['ModifiedSequence'].nunique():.1f}")
# RT distribution
import matplotlib.pyplot as plt
rts = library.groupby('ModifiedSequence')['NormalizedRetentionTime'].first()
plt.hist(rts, bins=50)
plt.xlabel('Normalized RT')
plt.ylabel('Precursors')
plt.savefig('rt_distribution.png')
# Charge state distribution
charges = library.groupby('ModifiedSequence')['PrecursorCharge'].first()
print(charges.value_counts())
Merge Libraries
import pandas as pd
# Load libraries
lib1 = pd.read_csv('library1.tsv', sep='\t')
lib2 = pd.read_csv('library2.tsv', sep='\t')
# Concatenate and remove duplicates
# Keep entry with highest total intensity per precursor
combined = pd.concat([lib1, lib2])
# Calculate total intensity per precursor
precursor_intensity = combined.groupby('ModifiedSequence')['LibraryIntensity'].sum()
# Keep best precursor entries
combined['total_int'] = combined['ModifiedSequence'].map(precursor_intensity)
combined = combined.sort_values('total_int', ascending=False)
combined = combined.drop_duplicates(subset=['ModifiedSequence', 'FragmentType', 'FragmentSeriesNumber'])
combined = combined.drop('total_int', axis=1)
combined.to_csv('merged_library.tsv', sep='\t', index=False)
iRT Calibration
# Biognosys iRT peptides for retention time calibration
IRT_PEPTIDES = {
'LGGNEQVTR': -24.92,
'GAGSSEPVTGLDAK': 0.00, # Reference
'VEATFGVDESNAK': 12.39,
'YILAGVENSK': 19.79,
'TPVISGGPYEYR': 28.71,
'TPVITGAPYEYR': 33.38,
'DGLDAASYYAPVR': 42.26,
'ADVTPADFSEWSK': 54.62,
'GTFIIDPGGVIR': 70.52,
'GTFIIDPAAVIR': 87.23,
'LFLQFGAQGSPFLK': 100.00
}
# Convert iRT to normalized RT
def irt_to_nrt(irt, gradient_length=60):
'''Convert iRT to normalized RT (0-1 scale)'''
return (irt + 24.92) / 124.92 # Scale to 0-1
Related Skills
- dia-analysis - Use libraries in DIA workflows
- peptide-identification - Generate search results for library building
- data-import - Load MS data for library generation
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