matchms

Name: matchms
Author: LeonChaoX

698stars61forks

How to install matchms?

leonchaox/qinyan-academic-skills/matchms

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill matchms

Installs into the current project.

Summary

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, spectral matching, library searc

Files · 1

View on GitHub

SKILL.md

1	# Matchms
2
3	## Overview
4
5	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.
6
7	## Core Capabilities
8
9	### 1. Importing and Exporting Mass Spectrometry Data
10
11	Load spectra from multiple file formats and export processed data:
12
13	```python
14	from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
15	from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
16
17	# Import spectra
18	spectra = list(load_from_mgf("spectra.mgf"))
19	spectra = list(load_from_mzml("data.mzML"))
20	spectra = list(load_from_msp("library.msp"))
21
22	# Export processed spectra
23	save_as_mgf(spectra, "output.mgf")
24	save_as_json(spectra, "output.json")
25	```
26
27	Supported formats:
28	- mzML and mzXML (raw mass spectrometry formats)
29	- MGF (Mascot Generic Format)
30	- MSP (spectral library format)
31	- JSON (GNPS-compatible)
32	- metabolomics-USI references
33	- Pickle (Python serialization)
34
35	For detailed importing/exporting documentation, consult `references/importing_exporting.md`.
36
37	### 2. Spectrum Filtering and Processing
38
39	Apply comprehensive filters to standardize metadata and refine peak data:
40
41	```python
42	from matchms.filtering import default_filters, normalize_intensities
43	from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
44
45	# Apply default metadata harmonization filters
46	spectrum = default_filters(spectrum)
47
48	# Normalize peak intensities
49	spectrum = normalize_intensities(spectrum)
50
51	# Filter peaks by relative intensity
52	spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
53
54	# Require minimum peaks
55	spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
56	```
57
58	Filter categories:
59	- Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
60	- Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks
61	- Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness
62	- Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches
63
64	Matchms provides 40+ filters. For the complete filter reference, consult `references/filtering.md`.
65
66	### 3. Calculating Spectral Similarities
67
68	Compare spectra using various similarity metrics:
69
70	```python
71	from matchms import calculate_scores
72	from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
73
74	# Calculate cosine similarity (fast, greedy algorithm)
75	scores = calculate_scores(references=library_spectra,
76	queries=query_spectra,
77	similarity_function=CosineGreedy())
78
79	# Calculate modified cosine (accounts for precursor m/z differences)
80	scores = calculate_scores(references=library_spectra,
81	queries=query_spectra,
82	similarity_function=ModifiedCosine(tolerance=0.1))
83
84	# Get best matches
85	best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]
86	```
87
88	Available similarity functions:
89	- CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms
90	- ModifiedCosine: Cosine similarity accounting for precursor mass differences
91	- NeutralLossesCosine: Similarity based on neutral loss patterns
92	- FingerprintSimilarity: Molecular structure similarity using fingerprints
93	- MetadataMatch: Compare user-defined metadata fields
94	- PrecursorMzMatch/ParentMassMatch: Simple mass-based filtering
95
96	For detailed similarity function documentation, consult `references/similarity.md`.
97
98	### 4. Building Processing Pipelines
99
100	Create reproducible, multi-step analysis workflows:
101
102	```python
103	from matchms import SpectrumProcessor
104	from matchms.filtering import default_filters, normalize_intensities
105	from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
106
107	# Define a processing pipeline
108	processor = SpectrumProcessor([
109	default_filters,
110	normalize_intensities,
111	lambda s: select_by_relative_intensity(s, intensity_from=0.01),
112	lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)
113	])
114
115	# Apply to all spectra
116	processed_spectra = [processor(s) for s in spectra]
117	```
118
119	### 5. Working with Spectrum Objects
120
121	The core `Spectrum` class contains mass spectral data:
122
123	```python
124	from matchms import Spectrum
125	import numpy as np
126
127	# Create a spectrum
128	mz = np.array([100.0, 150.0, 200

People who liked this also viewed

6 picks

Type

matchms

698stars61forks

View on GitHub

How to install matchms?

leonchaox/qinyan-academic-skills/matchms

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill matchms

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Matchms
2
3	## Overview
4
5	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.
6
7	## Core Capabilities
8
9	### 1. Importing and Exporting Mass Spectrometry Data
10
11	Load spectra from multiple file formats and export processed data:
12
13	```python
14	from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
15	from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
16
17	# Import spectra
18	spectra = list(load_from_mgf("spectra.mgf"))
19	spectra = list(load_from_mzml("data.mzML"))
20	spectra = list(load_from_msp("library.msp"))
21
22	# Export processed spectra
23	save_as_mgf(spectra, "output.mgf")
24	save_as_json(spectra, "output.json")
25	```
26
27	Supported formats:
28	- mzML and mzXML (raw mass spectrometry formats)
29	- MGF (Mascot Generic Format)
30	- MSP (spectral library format)
31	- JSON (GNPS-compatible)
32	- metabolomics-USI references
33	- Pickle (Python serialization)
34
35	For detailed importing/exporting documentation, consult `references/importing_exporting.md`.
36
37	### 2. Spectrum Filtering and Processing
38
39	Apply comprehensive filters to standardize metadata and refine peak data:
40
41	```python
42	from matchms.filtering import default_filters, normalize_intensities
43	from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
44
45	# Apply default metadata harmonization filters
46	spectrum = default_filters(spectrum)
47
48	# Normalize peak intensities
49	spectrum = normalize_intensities(spectrum)
50
51	# Filter peaks by relative intensity
52	spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
53
54	# Require minimum peaks
55	spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
56	```
57
58	Filter categories:
59	- Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
60	- Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks
61	- Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness
62	- Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches
63
64	Matchms provides 40+ filters. For the complete filter reference, consult `references/filtering.md`.
65
66	### 3. Calculating Spectral Similarities
67
68	Compare spectra using various similarity metrics:
69
70	```python
71	from matchms import calculate_scores
72	from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
73
74	# Calculate cosine similarity (fast, greedy algorithm)
75	scores = calculate_scores(references=library_spectra,
76	queries=query_spectra,
77	similarity_function=CosineGreedy())
78
79	# Calculate modified cosine (accounts for precursor m/z differences)
80	scores = calculate_scores(references=library_spectra,
81	queries=query_spectra,
82	similarity_function=ModifiedCosine(tolerance=0.1))
83
84	# Get best matches
85	best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]
86	```
87
88	Available similarity functions:
89	- CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms
90	- ModifiedCosine: Cosine similarity accounting for precursor mass differences
91	- NeutralLossesCosine: Similarity based on neutral loss patterns
92	- FingerprintSimilarity: Molecular structure similarity using fingerprints
93	- MetadataMatch: Compare user-defined metadata fields
94	- PrecursorMzMatch/ParentMassMatch: Simple mass-based filtering
95
96	For detailed similarity function documentation, consult `references/similarity.md`.
97
98	### 4. Building Processing Pipelines
99
100	Create reproducible, multi-step analysis workflows:
101
102	```python
103	from matchms import SpectrumProcessor
104	from matchms.filtering import default_filters, normalize_intensities
105	from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
106
107	# Define a processing pipeline
108	processor = SpectrumProcessor([
109	default_filters,
110	normalize_intensities,
111	lambda s: select_by_relative_intensity(s, intensity_from=0.01),
112	lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)
113	])
114
115	# Apply to all spectra
116	processed_spectra = [processor(s) for s in spectra]
117	```
118
119	### 5. Working with Spectrum Objects
120
121	The core `Spectrum` class contains mass spectral data:
122
123	```python
124	from matchms import Spectrum
125	import numpy as np
126
127	# Create a spectrum
128	mz = np.array([100.0, 150.0, 200

People who liked this also viewed

6 picks

Type