Add matchms

scientific-packages/matchms/SKILL.md

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+---
+name: matchms
+description: Process and analyze mass spectrometry data using matchms, a Python library for spectral similarity calculations, metadata harmonization, and compound identification. Use this skill when: (1) Working with mass spectrometry data files (mzML, mzXML, MGF, MSP, JSON) - importing, exporting, or converting between formats; (2) Compound identification tasks - matching unknown spectra against reference libraries using cosine similarity, modified cosine, or neutral loss patterns; (3) Spectral data preprocessing - harmonizing metadata, normalizing intensities, filtering peaks by m/z or intensity, removing precursor peaks, or applying quality control filters; (4) Building reproducible workflows - creating standardized processing pipelines, batch processing multiple datasets, or implementing consistent analysis protocols; (5) Chemical structure analysis - deriving SMILES/InChI from spectra, adding molecular fingerprints, validating structural annotations, or comparing structural similarities; (6) Large-scale spectral comparisons - performing library-to-library comparisons, finding duplicate spectra, or clustering similar compounds; (7) Multi-metric scoring - combining spectral similarity with structural similarity or metadata matching for robust compound identification; (8) Quality control and validation - filtering low-quality spectra, validating precursor masses, ensuring metadata completeness, or generating identification reports. This skill is essential for metabolomics, proteomics, natural products research, environmental analysis, and any field requiring mass spectrometry data processing and compound identification.
+---
+
+# Matchms
+
+## Overview
+
+Matchms is an open-source Python library for mass spectrometry data processing and analysis. It provides tools for importing spectra from various formats, standardizing metadata, filtering peaks, calculating spectral similarities, and building reproducible analytical workflows. The library democratizes mass spectrometry informatics through accessible, standardized Python tools.
+
+## 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
+pip install matchms
+```
+
+For molecular structure processing (SMILES, InChI):
+```bash
+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.