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Scientific Matchms
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification,...
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Your mass spectrometry experiment produced hundreds of unknown spectra that need to be matched against reference libraries for compound identification. matchms computes spectral similarity scores using cosine, modified cosine, and other metrics — turning raw spectra into identified metabolites with confidence scores.
Who it's for: metabolomics researchers identifying unknown compounds by matching experimental spectra to libraries, analytical chemists computing spectral similarity for quality control and compound verification, natural products chemists discovering novel metabolites through spectral networking, environmental scientists identifying pollutants from mass spectrometry data, pharmaceutical scientists screening compound libraries using spectral matching
Example
"Match our experimental MS/MS spectra against GNPS library to identify metabolites" → matchms pipeline: spectral data import and preprocessing with noise filtering, reference library loading from GNPS or MassBank, cosine and modified cosine similarity scoring, molecular networking from spectral similarities, and an identification report with matched compounds and confidence scores
CLAUDE.md Template
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# Matchms
## Overview
Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.
## Core Capabilities
### 1. Importing and Exporting Mass Spectrometry Data
Load spectra from multiple file formats and export processed data:
```python
from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
# Import spectra
spectra = list(load_from_mgf("spectra.mgf"))
spectra = list(load_from_mzml("data.mzML"))
spectra = list(load_from_msp("library.msp"))
# Export processed spectra
save_as_mgf(spectra, "output.mgf")
save_as_json(spectra, "output.json")
```
**Supported formats:**
- mzML and mzXML (raw mass spectrometry formats)
- MGF (Mascot Generic Format)
- MSP (spectral library format)
- JSON (GNPS-compatible)
- metabolomics-USI references
- Pickle (Python serialization)
For detailed importing/exporting documentation, consult `references/importing_exporting.md`.
### 2. Spectrum Filtering and Processing
Apply comprehensive filters to standardize metadata and refine peak data:
```python
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
# Apply default metadata harmonization filters
spectrum = default_filters(spectrum)
# Normalize peak intensities
spectrum = normalize_intensities(spectrum)
# Filter peaks by relative intensity
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
# Require minimum peaks
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
```
**Filter categories:**
- **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
- **Peak filtering**: Normalize intensities, select by m/z or intensity, remove precursor peaks
- **Quality control**: Require minimum peaks, validate precursor m/z, ensure metadata completeness
- **Chemical annotation**: Add fingerprints, derive InChI/SMILES, repair structural mismatches
Matchms provides 40+ filters. For the complete filter reference, consult `references/filtering.md`.
### 3. Calculating Spectral Similarities
Compare spectra using various similarity metrics:
```python
from matchms import calculate_scores
from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
# Calculate cosine similarity (fast, greedy algorithm)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=CosineGreedy())
# Calculate modified cosine (accounts for precursor m/z differences)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=ModifiedCosine(tolerance=0.1))
# Get best matches
best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]
```
**Available similarity functions:**
- **CosineGreedy/CosineHungarian**: Peak-based cosine similarity with different matching algorithms
- **ModifiedCosine**: Cosine similarity accounting for precursor mass differences
- **NeutralLossesCosine**: Similarity based on neutral loss patterns
- **FingerprintSimilarity**: Molecular structure similarity using fingerprints
- **MetadataMatch**: Compare user-defined metadata fields
- **PrecursorMzMatch/ParentMassMatch**: Simple mass-based filtering
For detailed similarity function documentation, consult `references/similarity.md`.
### 4. Building Processing Pipelines
Create reproducible, multi-step analysis workflows:
```python
from matchms import SpectrumProcessor
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
# Define a processing pipeline
processor = 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)
])
# Apply to all spectra
processed_spectra = [processor(s) for s in spectra]
```
### 5. Working with Spectrum Objects
The core `Spectrum` class contains mass spectral data:
```python
from matchms import Spectrum
import numpy as np
# Create a spectrum
mz = np.array([100.0, 150.0, 200.0, 250.0])
intensities = np.array([0.1, 0.5, 0.9, 0.3])
metadata = {"precursor_mz": 250.5, "ionmode": "positive"}
spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)
# Access spectrum properties
print(spectrum.peaks.mz) # m/z values
print(spectrum.peaks.intensities) # Intensity values
print(spectrum.get("precursor_mz")) # Metadata field
# Visualize spectra
spectrum.plot()
spectrum.plot_against(reference_spectrum)
```
### 6. Metadata Management
Standardize and harmonize spectrum metadata:
```python
# Metadata is automatically harmonized
spectrum.set("Precursor_mz", 250.5) # Gets harmonized to lowercase key
print(spectrum.get("precursor_mz")) # Returns 250.5
# Derive chemical information
from matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi
from matchms.filtering import add_fingerprint
spectrum = derive_inchi_from_smiles(spectrum)
spectrum = derive_inchikey_from_inchi(spectrum)
spectrum = add_fingerprint(spectrum, fingerprint_type="morgan", nbits=2048)
```
## Common Workflows
For typical mass spectrometry analysis workflows, including:
- Loading and preprocessing spectral libraries
- Matching unknown spectra against reference libraries
- Quality filtering and data cleaning
- Large-scale similarity comparisons
- Network-based spectral clustering
Consult `references/workflows.md` for detailed examples.
## Installation
```bash
uv pip install matchms
```
For molecular structure processing (SMILES, InChI):
```bash
uv pip install matchms[chemistry]
```
## Reference Documentation
Detailed reference documentation is available in the `references/` directory:
- `filtering.md` - Complete filter function reference with descriptions
- `similarity.md` - All similarity metrics and when to use them
- `importing_exporting.md` - File format details and I/O operations
- `workflows.md` - Common analysis patterns and examples
Load these references as needed for detailed information about specific matchms capabilities.Get new playbooks like this one
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README.md
What This Does
Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.
Quick Start
Step 1: Create a Project Folder
mkdir -p ~/Projects/matchms
Step 2: Download the Template
Click Download above, then:
mv ~/Downloads/CLAUDE.md ~/Projects/matchms/
Step 3: Start Claude Code
cd ~/Projects/matchms
claude
Core Capabilities
1. Importing and Exporting Mass Spectrometry Data
Load 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
from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
# Import spectra
spectra = list(load_from_mgf("spectra.mgf"))
spectra = list(load_from_mzml("data.mzML"))
spectra = list(load_from_msp("library.msp"))
# Export processed spectra
save_as_mgf(spectra, "output.mgf")
save_as_json(spectra, "output.json")
Supported formats:
- mzML and mzXML (raw mass spectrometry formats)
- MGF (Mascot Generic Format)
- MSP (spectral library format)
- JSON (GNPS-compatible)
- metabolomics-USI references
- Pickle (Python serialization)
For detailed importing/exporting documentation, consult references/importing_exporting.md.
2. Spectrum Filtering and Processing
Apply comprehensive filters to standardize metadata and refine peak data:
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
# Apply default metadata harmonization filters
spectrum = default_filters(spectrum)
# Normalize peak intensities
spectrum = normalize_intensities(spectrum)
# Filter peaks by relative intensity
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
# Require minimum peaks
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
Filter categories:
- Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
- Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks
- Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness
- Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches
Matchms provides 40+ filters. For the complete filter reference, consult references/filtering.md.
3. Calculating Spectral Similarities
Compare spectra using various similarity metrics:
from matchms import calculate_scores
from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
# Calculate cosine similarity (fast, greedy algorithm)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=CosineGreedy())
# Calculate modified cosine (accounts for precursor m/z differences)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=ModifiedCosine(tolerance=0.1))
# Get best matches
best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]
Available similarity functions:
- CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms
- ModifiedCosine: Cosine similarity accounting for precursor mass differences
- NeutralLossesCosine: Similarity based on neutral loss patterns
- FingerprintSimilarity: Molecular structure similarity using fingerprints
- MetadataMatch: Compare user-defined metadata fields
- PrecursorMzMatch/ParentMassMatch: Simple mass-based filtering
For detailed similarity function documentation, consult references/similarity.md.
4. Building Processing Pipelines
Create reproducible, multi-step analysis workflows:
from matchms import SpectrumProcessor
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
# Define a processing pipeline
processor = 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)
])
# Apply to all spectra
processed_spectra = [processor(s) for s in spectra]
5. Working with Spectrum Objects
The core Spectrum class contains mass spectral data:
from matchms import Spectrum
import numpy as np
# Create a spectrum
mz = np.array([100.0, 150.0, 200.0, 250.0])
intensities = np.array([0.1, 0.5, 0.9, 0.3])
metadata = {"precursor_mz": 250.5, "ionmode": "positive"}
spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)
# Access spectrum properties
print(spectrum.peaks.mz) # m/z values
print(spectrum.peaks.intensities) # Intensity values
print(spectrum.get("precursor_mz")) # Metadata field
# Visualize spectra
spectrum.plot()
spectrum.plot_against(reference_spectrum)
6. Metadata Management
Standardize and harmonize spectrum metadata:
# Metadata is automatically harmonized
spectrum.set("Precursor_mz", 250.5) # Gets harmonized to lowercase key
print(spectrum.get("precursor_mz")) # Returns 250.5
# Derive chemical information
from matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi
from matchms.filtering import add_fingerprint
spectrum = derive_inchi_from_smiles(spectrum)
spectrum = derive_inchikey_from_inchi(spectrum)
spectrum = add_fingerprint(spectrum, fingerprint_type="morgan", nbits=2048)
Common Workflows
For typical mass spectrometry analysis workflows, including:
- Loading and preprocessing spectral libraries
- Matching unknown spectra against reference libraries
- Quality filtering and data cleaning
- Large-scale similarity comparisons
- Network-based spectral clustering
Consult references/workflows.md for detailed examples.
Installation
uv pip install matchms
For molecular structure processing (SMILES, InChI):
uv pip install matchms[chemistry]
Reference Documentation
Detailed reference documentation is available in the references/ directory:
filtering.md- Complete filter function reference with descriptionssimilarity.md- All similarity metrics and when to use themimporting_exporting.md- File format details and I/O operationsworkflows.md- Common analysis patterns and examples
Load these references as needed for detailed information about specific matchms capabilities.
Tips
- Read the docs: Check the official matchms documentation for latest API changes
- Start simple: Begin with basic examples before tackling complex workflows
- Save your work: Keep intermediate results in case of long-running analyses