Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Preprocessing and feature extraction

TidyMS

A Python library for reading, processing, curating, and visualizing mass-spectrometry data in metabolomics, including LC-MS feature detection, correspondence, and data-matrix curation.

LC-MSUntargeted metabolomicsPython packageOpen source
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Overview

TidyMS provides Python building blocks for reading, processing and visualizing metabolomics MS data. It supports chromatogram/spectrum construction, peak detection, LC-MS feature correspondence and curation of processed data matrices.

What can this tool do?

Chromatogram buildingPeak or feature detectionRetention-time alignment

Common uses

  • Detect and align LC-MS features.
  • Curate metabolomics data matrices and inspect raw or processed data.

Scientific fit

Primary function

Preprocessing and feature extraction

Other functions

Quality control and filtering, Visualization and reporting

Analysis types

Untargeted metabolomics

Data compatibility

Inputs

Raw instrument data, Feature or peak table

Outputs

Feature or peak table, Human-readable report

Input formats

mzML files or supported processed tables imported into TidyMS objects

Output formats

Python feature/data-matrix objects and plotting objects

Scope and considerations

Designed for

  • A Python library for reading, processing, curating, and visualizing mass-spectrometry data in metabolomics, including LC-MS feature detection, correspondence, and data-matrix curation.

Not designed for

  • A turnkey structure-identification service or automatic applicability of one peak-picking configuration to every acquisition.

Important requirements

  • A Python environment with TidyMS dependencies, mzML raw data or supported processed feature tables, and analysis-specific parameters chosen through the documented API/notebook examples.

Considerations

  • Upstream conversion, peak detection and data-matrix filtering influence results. Validate correspondence and filtering decisions on representative samples.

Access and documentation

Primary accessDocumentation

BSD-3-Clause Python library with documentation and example notebooks. It is an API for composing workflows, so users must select and record processing steps.

License: BSD-3-Clause

Evidence and review

Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows. Metabolites (2020). doi:10.3390/metabo10100416

Related tools

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Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 27 source-reviewed tools from Enveda intake

    View change · Entry at this revision

Full GitHub history