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