Build and manage spectral libraries, reference collections for identifying peptides in proteomics data. Use measured or predicted spectra, convert library formats, check quality, and merge libraries for DIA, a mass spectrometry acquisition method.
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Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Reference examples tested with: matplotlib 3.8+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Build a spectral library for DIA analysis" → Create, filter, and manage spectral libraries from DDA experiments or predicted spectra for use in DIA quantification workflows.
spectrast (TPP) for consensus library building from search resultspandas for library format conversion and quality filtering# 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
# 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
# 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
# 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()
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}))
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)
# Required columns
PrecursorMz ProductMz Annotation ProteinId GeneName
PeptideSequence ModifiedSequence PrecursorCharge
FragmentCharge FragmentType FragmentSeriesNumber
NormalizedRetentionTime LibraryIntensity
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)
# Key columns for Spectronaut
ModifiedPeptide StrippedPeptide PrecursorCharge
PrecursorMz iRT FragmentLossType
FragmentCharge FragmentType FragmentNumber
RelativeIntensity FragmentMz ProteinGroups
Genes ProteinIds
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())
Goal: Combine multiple spectral libraries into a single non-redundant library, keeping the highest-quality spectra for each precursor.
Approach: Concatenate library tables, rank precursors by total fragment intensity, and deduplicate by keeping the best-scoring entry per precursor-fragment combination.
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)
# 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
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