matchms-spectral-matching

name	matchms-spectral-matching
description	MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
license	Apache-2.0

Matchms — Spectral Matching & Metabolite Identification

Overview

Matchms is a Python library for mass spectrometry data processing focused on spectral similarity calculation and compound identification. It provides multi-format I/O, 50+ spectrum filters for metadata harmonization and peak processing, 8 similarity scoring functions, and a pipeline framework for reproducible analytical workflows.

When to Use

• Identifying unknown metabolites by matching MS/MS spectra against reference libraries
• Computing spectral similarity scores (cosine, modified cosine, fingerprint-based)
• Processing and standardizing mass spectral data from multiple formats (mzML, MGF, MSP, JSON)
• Building reproducible spectral processing pipelines for quality control
• Harmonizing metadata across spectral databases (compound names, SMILES, InChI, adducts)
• Large-scale spectral library comparisons and duplicate detection
• For full LC-MS/MS proteomics workflows (feature detection, protein ID), use pyopenms instead
• For chemical structure similarity without mass spectra, use rdkit fingerprint comparison

Prerequisites

uv pip install matchms numpy pandas
# For chemical structure processing (SMILES, InChI, fingerprints):
uv pip install matchms[chemistry]

• Python 3.8+; NumPy for peak array operations
• Input: spectral data in MGF, MSP, mzML, mzXML, JSON, or pickle format
• Reference library in any supported format for matching

Quick Start

from matchms.importing import load_from_mgf
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
from matchms import calculate_scores
from matchms.similarity import CosineGreedy

# Load and process query spectra
queries = list(load_from_mgf("queries.mgf"))
queries = [default_filters(s) for s in queries]
queries = [normalize_intensities(s) for s in queries if s is not None]
queries = [require_minimum_number_of_peaks(s, n_required=5) for s in queries if s is not None]

# Load reference library
refs = list(load_from_mgf("library.mgf"))
refs = [default_filters(s) for s in refs]
refs = [normalize_intensities(s) for s in refs if s is not None]

# Calculate similarity scores
scores = calculate_scores(references=refs, queries=queries,
                          similarity_function=CosineGreedy(tolerance=0.1))
# Get best matches for first query
best = scores.scores_by_query(queries[0], sort=True)[:5]
 , score_tuple  best:
    ()

Core API

Module 1: Spectrum I/O

Import spectra from multiple file formats and export processed data.

from matchms.importing import (load_from_mgf, load_from_mzml, load_from_msp,
                                load_from_json, load_from_mzxml, load_from_pickle,
                                load_from_usi)
from matchms.exporting import save_as_mgf, save_as_msp, save_as_json, save_as_pickle

# Import from various formats (returns generators)
spectra_mgf = list(load_from_mgf("library.mgf"))
spectra_mzml = list(load_from_mzml("data.mzML"))
spectra_msp = list(load_from_msp("nist_library.msp"))
spectra_json = list(load_from_json("gnps_spectra.json"))
print(f"Loaded: MGF={len(spectra_mgf)}, mzML={len(spectra_mzml)}")

# Export processed spectra
save_as_mgf(spectra_mgf, "processed.mgf")
save_as_json(spectra_mgf, "processed.json")
save_as_pickle(spectra_mgf, "spectra.pickle")  # Fast for intermediate results

# Pickle for large datasets (fastest I/O)
from matchms.importing import load_from_pickle
spectra = list(load_from_pickle("spectra.pickle"))

from matchms import Spectrum
import numpy as np

# Create spectrum manually
mz = np.array([100.0, 150.0, 200.0, 250.0, 300.0])
intensities = np.array([0.1, 0.5, 0.9, 0.3, 0.7])
metadata = {
    "precursor_mz": 325.5,
    "ionmode": "positive",
    "compound_name": "Caffeine",
    "smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"
}
spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)

# Access spectrum data
print(f"Peaks: {spectrum.peaks.mz}")
print(f"Precursor: {spectrum.get('precursor_mz')}")
print(f"Name: {spectrum.get('compound_name')}")

# Visualize
spectrum.plot()

Module 2: Spectrum Filtering & Processing

Apply metadata harmonization and peak processing filters. Matchms provides 50+ filters.

from matchms.filtering import (
    default_filters, normalize_intensities,
    select_by_relative_intensity, select_by_mz,
    require_minimum_number_of_peaks, reduce_to_number_of_peaks,
    remove_peaks_around_precursor_mz, add_losses
)

# default_filters applies: metadata cleanup, charge correction, adduct parsing
spectrum = default_filters(spectrum)

# Peak normalization (max intensity → 1.0)
spectrum = normalize_intensities(spectrum)

# Filter peaks by relative intensity (remove noise below 1%)
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)

# Filter peaks by m/z range
spectrum = select_by_mz(spectrum, mz_from=50.0, mz_to=500.0)

# Keep top N peaks only
spectrum = reduce_to_number_of_peaks(spectrum, n_max=50)

# Remove peaks near precursor (common contaminants)
spectrum = remove_peaks_around_precursor_mz(spectrum, mz_tolerance=17.0)

# Require minimum peaks for matching
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)

# Add neutral losses (useful for NeutralLossesCosine)
spectrum = add_losses(spectrum)

if spectrum is not None:
    print(f"After filtering: {len(spectrum.peaks.mz)} peaks")

# Chemical annotation filters (require matchms[chemistry])
from matchms.filtering import (
    derive_inchi_from_smiles, derive_inchikey_from_inchi,
    derive_smiles_from_inchi, add_fingerprint,
    repair_inchi_inchikey_smiles, require_valid_annotation
)

# Derive chemical identifiers from SMILES
spectrum = derive_inchi_from_smiles(spectrum)
spectrum = derive_inchikey_from_inchi(spectrum)

# Add molecular fingerprint for structural similarity
spectrum = add_fingerprint(spectrum, fingerprint_type="morgan", nbits=2048)

# Validate annotations
spectrum = require_valid_annotation(spectrum)
if spectrum is not None:
    print(f"InChIKey: {spectrum.get('inchikey')}")

Module 3: Similarity Scoring

Compare spectra using multiple similarity metrics.

from matchms import calculate_scores
from matchms.similarity import (
    CosineGreedy, CosineHungarian, ModifiedCosine,
    NeutralLossesCosine, FingerprintSimilarity,
    MetadataMatch, PrecursorMzMatch
)

# CosineGreedy — fast peak matching (greedy algorithm)
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=CosineGreedy(tolerance=0.1))

# ModifiedCosine — accounts for precursor mass differences (best for analog search)
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=ModifiedCosine(tolerance=0.1))

# CosineHungarian — optimal peak matching (slower but more accurate)
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=CosineHungarian(tolerance=0.1))

# NeutralLossesCosine — similarity based on neutral loss patterns
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=NeutralLossesCosine(tolerance=0.1))

# Access results
for i, query in enumerate(unknowns[:3]):
    best_matches = scores.scores_by_query(query, sort=True)[:3]
    print(f"\nQuery {i}: precursor_mz={query.get('precursor_mz')}")
    for ref, score_tuple in best_matches:
        print(f"  {ref.get('compound_name', 'Unknown')}: "
              f"score={score_tuple['score']:.3f}, matches={score_tuple['matches']}")

# FingerprintSimilarity — structural similarity (requires fingerprints)
from matchms.similarity import FingerprintSimilarity
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=FingerprintSimilarity(
                              similarity_measure="jaccard"))

# PrecursorMzMatch — fast mass-based pre-filtering
from matchms.similarity import PrecursorMzMatch
scores = calculate_scores(references=library, queries=unknowns,
                          similarity_function=PrecursorMzMatch(tolerance=0.1))

# Multi-metric scoring: combine peak + structural similarity
cosine_scores = calculate_scores(references=library, queries=unknowns,
                                  similarity_function=CosineGreedy(tolerance=0.1))
fp_scores = calculate_scores(references=library, queries=unknowns,
                              similarity_function=FingerprintSimilarity())

Module 4: Processing Pipelines

Build reusable, reproducible multi-step processing workflows.

from matchms import SpectrumProcessor
from matchms.filtering import (
    default_filters, normalize_intensities,
    select_by_relative_intensity, require_minimum_number_of_peaks,
    remove_peaks_around_precursor_mz, add_losses
)

# Define reusable pipeline
pipeline = SpectrumProcessor([
    default_filters,
    normalize_intensities,
    lambda s: select_by_relative_intensity(s, intensity_from=0.01),
    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17.0),
    lambda s: require_minimum_number_of_peaks(s, n_required=5),
    add_losses
])

# Apply to all spectra (filters returning None remove the spectrum)
processed = [pipeline(s) for s in raw_spectra]
processed = [s for s in processed if s is not None]
print(f"Processed: {len(processed)}/{len(raw_spectra)} spectra retained")

Key Concepts

Similarity Function Comparison

Function	Speed	Accuracy	Best For
CosineGreedy	Fast	Good	General library matching
CosineHungarian	Slow	Best	Small comparisons, validation
ModifiedCosine	Fast	Good	Analog search (different precursors)
NeutralLossesCosine	Medium	Good	Structural class identification
FingerprintSimilarity	Fast	Moderate	Structure-based pre-filtering
PrecursorMzMatch	Fastest	N/A	Mass-based pre-filtering

Filter Categories

Category	Examples	Purpose
Metadata cleanup	default_filters, clean_compound_name, clean_adduct	Standardize metadata fields
Chemical derivation	derive_inchi_from_smiles, add_fingerprint	Compute chemical identifiers
Mass/charge	add_precursor_mz, correct_charge, add_parent_mass	Fix and validate mass info
Peak normalization	normalize_intensities, select_by_relative_intensity	Scale and filter peaks
Peak reduction	reduce_to_number_of_peaks, remove_peaks_around_precursor_mz	Remove noise/artifacts
Quality control	require_minimum_number_of_peaks, require_precursor_mz	Enforce minimum quality
Neutral losses	add_losses	Compute precursor-fragment losses

Score Tuple Structure

All similarity functions return (score, matches):

• score: float 0.0–1.0 (cosine similarity value)
• matches: int (number of matched peaks between query and reference)

Higher scores and more matched peaks indicate better matches. Typical thresholds: score > 0.7 and matches > 6 for confident identifications.

Common Workflows

Workflow 1: Library Matching for Metabolite Identification

from matchms.importing import load_from_mgf
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
from matchms import calculate_scores
from matchms.similarity import ModifiedCosine
import pandas as pd

# Load and process both queries and library identically
def process_spectra(spectra):
    processed = []
    for s in spectra:
        s = default_filters(s)
        if s is None: continue
        s = normalize_intensities(s)
        s = select_by_relative_intensity(s, intensity_from=0.01)
        s = require_minimum_number_of_peaks(s, n_required=5)
        if s is not None:
            processed.append(s)
    return processed

queries = process_spectra(load_from_mgf("unknowns.mgf"))
library = process_spectra(load_from_mgf("reference_library.mgf"))
print(f"Queries: {len(queries)}, Library: {len(library)}")

# Score all query-reference pairs
scores = calculate_scores(references=library, queries=queries,
                          similarity_function=ModifiedCosine(tolerance=0.1))

# Extract best matches
results = []
for query in queries:
    best = scores.scores_by_query(query, sort=)[:]
     best:
        ref, score_tuple = best[]
        results.append({
            : query.get(),
            : ref.get(, ),
            : ref.get(, ),
            : score_tuple[],
            : score_tuple[]
        })

df = pd.DataFrame(results)
confident = df[df[] > ]
()
df.to_csv(, index=)

Workflow 2: Quality Control and Data Cleaning

from matchms.importing import load_from_msp
from matchms.exporting import save_as_mgf
from matchms import SpectrumProcessor
from matchms.filtering import (
    default_filters, normalize_intensities,
    select_by_relative_intensity, require_minimum_number_of_peaks,
    require_precursor_mz, add_parent_mass
)

# Define QC pipeline
qc_pipeline = SpectrumProcessor([
    default_filters,
    require_precursor_mz,
    add_parent_mass,
    normalize_intensities,
    lambda s: select_by_relative_intensity(s, intensity_from=0.001),
    lambda s: require_minimum_number_of_peaks(s, n_required=3)
])

# Process and filter
raw = list(load_from_msp("raw_library.msp"))
cleaned = [qc_pipeline(s) for s in raw]
cleaned = [s for s in cleaned if s is not None]

print(f"Input: {len(raw)}, Output: {len(cleaned)} ({len(cleaned)/len(raw)*100:.1f}% retained)")
save_as_mgf(cleaned, "cleaned_library.mgf")

Workflow 3: Format Conversion

1. Load spectra from source format (Core API Module 1 — load_from_mzml)
2. Apply default_filters for metadata harmonization (Core API Module 2)
3. Export to target format (Core API Module 1 — save_as_mgf)

Key Parameters

Parameter	Function/Module	Default	Range/Options	Effect
tolerance	CosineGreedy/ModifiedCosine	0.1	0.005–0.5 Da	m/z tolerance for peak matching
mz_power	CosineGreedy	0.0	0.0–2.0	Weight of m/z in scoring (0=ignore)
intensity_power	CosineGreedy	1.0	0.0–2.0	Weight of intensity in scoring
intensity_from	select_by_relative_intensity	0.0	0.0–1.0	Minimum relative intensity to keep
n_required	require_minimum_number_of_peaks	10	1–100	Minimum peaks to retain spectrum
n_max	reduce_to_number_of_peaks	100	10–500	Maximum peaks to retain
mz_tolerance	remove_peaks_around_precursor_mz	17.0	0.5–50 Da	Window around precursor to remove
fingerprint_type	add_fingerprint	"daylight"	"daylight"/"morgan"/"maccs"	Molecular fingerprint type
nbits	add_fingerprint	2048	256–4096	Fingerprint bit vector length

Best Practices

1. Always process queries and references identically — apply the same filtering pipeline to both sets to avoid systematic bias in similarity scores
2. Save intermediate results in pickle — pickle format is fastest for re-loading; use MGF/MSP for sharing with other tools
3. Pre-filter by precursor mass — for large libraries, use PrecursorMzMatch first to reduce the comparison space, then score with CosineGreedy
4. Combine multiple metrics — use both CosineGreedy (peak matching) and FingerprintSimilarity (structure) for more robust identification
5. Check for None after filtering — filters return None when a spectrum fails quality requirements. Always filter: [s for s in processed if s is not None]
6. Use ModifiedCosine for analog search — when querying against libraries that may not have the exact compound, ModifiedCosine handles precursor mass differences

Common Recipes

Recipe 1: Precursor-Filtered Library Search (Efficient)

from matchms import calculate_scores
from matchms.similarity import PrecursorMzMatch, CosineGreedy

# Step 1: Fast mass filter
mass_scores = calculate_scores(references=library, queries=unknowns,
                                similarity_function=PrecursorMzMatch(tolerance=0.5))

# Step 2: Detailed scoring only for mass-matched pairs
cosine = CosineGreedy(tolerance=0.1)
for query in unknowns:
    candidates = mass_scores.scores_by_query(query, sort=True)
    mass_matched = [ref for ref, score in candidates if score["score"] > 0]
    if mass_matched:
        detailed = calculate_scores(references=mass_matched, queries=[query],
                                     similarity_function=cosine)
        best = detailed.scores_by_query(query, sort=True)[:3]
        for ref, s in best:
            print(f"{ref.get('compound_name')}: {s['score']:.3f}")

Recipe 2: Ion Mode-Specific Processing

from matchms.importing import load_from_mgf
from matchms.filtering import default_filters, normalize_intensities

spectra = list(load_from_mgf("mixed_library.mgf"))
spectra = [default_filters(s) for s in spectra]
spectra = [s for s in spectra if s is not None]

# Separate by ion mode
positive = [s for s in spectra if s.get("ionmode") == "positive"]
negative = [s for s in spectra if s.get("ionmode") == "negative"]
print(f"Positive: {len(positive)}, Negative: {len(negative)}")

# Process each mode with mode-specific filtering
positive = [normalize_intensities(s) for s in positive]
negative = [normalize_intensities(s) for s in negative]

Recipe 3: Metadata Enrichment Report

import pandas as pd
from matchms.importing import load_from_mgf
from matchms.filtering import default_filters

spectra = [default_filters(s) for s in load_from_mgf("library.mgf")]
spectra = [s for s in spectra if s is not None]

# Extract metadata summary
rows = []
for s in spectra:
    rows.append({
        "compound_name": s.get("compound_name", ""),
        "precursor_mz": s.get("precursor_mz"),
        "ionmode": s.get("ionmode", ""),
        "smiles": s.get("smiles", ""),
        "inchikey": s.get("inchikey", ""),
        "num_peaks": len(s.peaks.mz)
    })

df = pd.DataFrame(rows)
print(f"Library: {len(df)} spectra")
print(f"Named: {(df.compound_name != '').sum()}")
print(f"With SMILES: {(df.smiles != '').sum()}")
print(f"Ion modes: {df.ionmode.value_counts().to_dict()}")

Troubleshooting

Problem	Cause	Solution
All scores are 0.0	No matching peaks within tolerance	Increase tolerance (try 0.2–0.5 Da); verify both spectra have peaks
Low scores despite same compound	Different fragmentation conditions	Use ModifiedCosine instead of CosineGreedy; check ion mode consistency
Many spectra filtered to None	Too strict quality filters	Lower n_required in require_minimum_number_of_peaks; relax intensity thresholds
KeyError on metadata field	Field name not harmonized	Apply default_filters first to harmonize metadata keys
Memory error with large library	All-vs-all comparison	Pre-filter by precursor mass (PrecursorMzMatch) before detailed scoring
add_fingerprint fails	RDKit not installed	Install chemistry extras: pip install matchms[chemistry]
Import returns empty list	Wrong file format or path	Verify format matches loader (MGF for .mgf, MSP for .msp); check file is not empty
Inconsistent scores between runs	Different processing pipelines	Use SpectrumProcessor to ensure identical processing for queries and references

Bundled Resources

• references/filtering_catalog.md — Complete catalog of 50+ matchms filter functions organized by category (metadata processing, chemical structure, mass/charge, peak processing, quality control).

  • Covers: all filter function signatures with parameters and brief descriptions, common filter combinations
  • Relocated inline: key filters (default_filters, normalize_intensities, select_by_relative_intensity, reduce_to_number_of_peaks, remove_peaks_around_precursor_mz, add_fingerprint) in Core API Module 2; filter category summary in Key Concepts
  • Omitted: individual filter examples duplicating Core API patterns

• references/workflows_similarity.md — Extended workflows and detailed similarity function documentation consolidated from two original references.

  • Covers: all 8 similarity functions with parameter tables, large-scale comparison strategies, multi-metric scoring patterns, 7 extended workflows (library matching, QC, multi-metric, format conversion, metadata enrichment, large-scale comparison, automated identification report), performance considerations
  • Relocated inline: CosineGreedy/ModifiedCosine/CosineHungarian/FingerprintSimilarity usage in Core API Module 3; similarity comparison table in Key Concepts; library matching + QC workflows in Common Workflows
  • Omitted: network-based spectral clustering workflow — requires external tool (spec2vec); spectra visualization tutorial — covered by spectrum.plot() in Core API Module 1

Disposition of original reference files:

• importing_exporting.md (417 lines) → fully consolidated into Core API Module 1 (I/O functions, format list, Spectrum creation, pickle usage). Retained: all 7 import functions, 4 export functions, Spectrum class creation, format selection guidance. Omitted: USI detailed examples — niche use case
• filtering.md (289 lines) → migrated as references/filtering_catalog.md with key filters relocated to Core API Module 2
• similarity.md (381 lines) → consolidated into references/workflows_similarity.md with core functions in Core API Module 3
• workflows.md (648 lines) → consolidated into references/workflows_similarity.md with top workflows in Common Workflows

Related Skills

• pyopenms-mass-spectrometry — full LC-MS/MS proteomics and metabolomics pipelines (feature detection, protein ID)
• rdkit-cheminformatics — molecular fingerprint generation and chemical structure similarity

References

• matchms documentation: https://matchms.readthedocs.io
• Huber et al. (2020) matchms — processing and similarity scoring of mass spectrometry data. Journal of Open Source Software, DOI: 10.21105/joss.02411
• GitHub: https://github.com/matchms/matchms