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matchms：Python质谱数据处理分析库，支持谱图导入、过滤、相似度计算

matchms by k-dense-ai/claude-scientific-skills

54 周安装量

17,300 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill matchms

科研工具生物信息学数据处理

🇨🇳中文介绍

Matchms

概述

Matchms 是一个用于质谱数据处理和分析的开源 Python 库。支持从多种格式导入谱图、标准化元数据、过滤峰、计算谱图相似度，并构建可复现的分析工作流。

核心功能

1. 质谱数据的导入与导出

从多种文件格式加载谱图并导出处理后的数据：

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

# 导入谱图
spectra = list(load_from_mgf("spectra.mgf"))
spectra = list(load_from_mzml("data.mzML"))
spectra = list(load_from_msp("library.msp"))

# 导出处理后的谱图
save_as_mgf(spectra, "output.mgf")
save_as_json(spectra, "output.json")

支持的格式：

mzML 和 mzXML（原始质谱数据格式）
MGF（Mascot 通用格式）
MSP（谱图库格式）
JSON（兼容 GNPS）
metabolomics-USI 引用
Pickle（Python 序列化格式）

有关导入/导出的详细文档，请查阅 references/importing_exporting.md。

2. 谱图过滤与处理

应用全面的过滤器来标准化元数据并精炼峰数据：

from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks

# 应用默认的元数据协调过滤器
spectrum = default_filters(spectrum)

# 归一化峰强度
spectrum = normalize_intensities(spectrum)

# 按相对强度过滤峰
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)

# 要求最小峰数
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)

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3. 计算谱图相似度

使用各种相似度度量比较谱图：

from matchms import calculate_scores
from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian

# 计算余弦相似度（快速、贪婪算法）
scores = calculate_scores(references=library_spectra,
                         queries=query_spectra,
                         similarity_function=CosineGreedy())

# 计算修正余弦相似度（考虑前体 m/z 差异）
scores = calculate_scores(references=library_spectra,
                         queries=query_spectra,
                         similarity_function=ModifiedCosine(tolerance=0.1))

# 获取最佳匹配
best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]

可用的相似度函数：

CosineGreedy/CosineHungarian：基于峰的余弦相似度，采用不同的匹配算法
ModifiedCosine：考虑前体质量差异的余弦相似度
NeutralLossesCosine：基于中性丢失模式的相似度
FingerprintSimilarity：使用指纹的分子结构相似度
MetadataMatch：比较用户定义的元数据字段
PrecursorMzMatch/ParentMassMatch：基于质量的简单过滤

有关相似度函数的详细文档，请查阅 references/similarity.md。

4. 构建处理流程

创建可复现的多步骤分析工作流：

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

# 定义一个处理流程
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)
])

# 应用于所有谱图
processed_spectra = [processor(s) for s in spectra]

5. 使用 Spectrum 对象

核心的 Spectrum 类包含质谱数据：

from matchms import Spectrum
import numpy as np

# 创建一个谱图
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)

# 访问谱图属性
print(spectrum.peaks.mz)           # m/z 值
print(spectrum.peaks.intensities)  # 强度值
print(spectrum.get("precursor_mz")) # 元数据字段

# 可视化谱图
spectrum.plot()
spectrum.plot_against(reference_spectrum)

标准化和协调谱图元数据：

# 元数据会自动协调
spectrum.set("Precursor_mz", 250.5)  # 会被协调为小写键
print(spectrum.get("precursor_mz"))   # 返回 250.5

# 推导化学信息
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)

针对典型的质谱分析工作流，包括：

加载和预处理谱图库
将未知谱图与参考库进行匹配
质量过滤和数据清洗
大规模相似度比较
基于网络的谱图聚类

详细示例请查阅 references/workflows.md。

uv pip install matchms

如需分子结构处理（SMILES, InChI）：

uv pip install matchms[chemistry]

详细的参考文档可在 references/ 目录中找到：

filtering.md - 完整的过滤器函数参考及描述
similarity.md - 所有相似度度量及其适用场景
importing_exporting.md - 文件格式详情和 I/O 操作
workflows.md - 常见分析模式和示例

如需了解 matchms 特定功能的详细信息，请根据需要加载这些参考文档。

🇺🇸English

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:

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 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.

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Repository

k-dense-ai/clau…c-skills

GitHub Stars

17.3K

First Seen

Jan 20, 2026

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1,300 周安装

matchms：Python质谱数据处理分析库，支持谱图导入、过滤、相似度计算

🇨🇳中文介绍

Matchms

概述

核心功能

1. 质谱数据的导入与导出

2. 谱图过滤与处理

相关 Skills

3. 计算谱图相似度

4. 构建处理流程

5. 使用 Spectrum 对象

6. 元数据管理

常见工作流

安装

参考文档

🇺🇸English

Matchms

Overview

Core Capabilities

1. Importing and Exporting Mass Spectrometry Data

2. Spectrum Filtering and Processing

3. Calculating Spectral Similarities

4. Building Processing Pipelines

5. Working with Spectrum Objects

6. Metadata Management

Common Workflows

Installation

Reference Documentation

最新 Skills