bio-proteomics-spectral-libraries

Version Compatibility

Reference examples tested with: matplotlib 3.8+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Spectral Library Management

"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.

• CLI: spectrast (TPP) for consensus library building from search results
• CLI: Prosit/DeepLC for deep learning-predicted spectral libraries
• Python: pandas for library format conversion and quality filtering

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

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)

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