Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Workflow management and reproducibility

Python-Centric Pipeline for Metabolomics PCPFM

An open Python workflow for LC-MS metabolomics that converts supported raw data, performs feature extraction with Asari, applies QC and normalization, groups empirical compounds, and supports MS1/MS2 annotation.

LC-MSUntargeted metabolomicsCommand linePython packageAccess model unclear
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Overview

The Python-Centric Pipeline for Metabolomics organizes sample metadata, Asari-based preprocessing, QC and empirical-compound annotation in a common data model. Its command-line stages preserve intermediate analyses and export tables for downstream interpretation.

What can this tool do?

Provenance trackingPeak or feature detectionBlank and contaminant filteringBatch-effect correctionAdduct and isotope groupingSpectral-library matching

Common uses

  • Run a reproducible LC-MS metabolomics workflow from raw or mzML data to curated feature tables.
  • Combine feature extraction, QC, normalization, empirical-compound grouping, and annotation in one Python workflow.

Scientific fit

Primary function

Workflow management and reproducibility

Other functions

Data conversion and import, Preprocessing and feature extraction, Quality control and filtering, Normalization and batch correction, Metabolite annotation and identification

Analysis types

Untargeted metabolomics

MS level

MS1, MS/MS

Acquisition

Full-scan MS, Data-dependent acquisition (DDA), General or unspecified acquisition

Ion mobility

Not specified

Data compatibility

Inputs

Raw instrument data, Sample metadata

Outputs

Feature or peak table, Compound list or annotations, Human-readable report

Input formats

Vendor raw format, mzML, CSV or TSV

Output formats

CSV or TSV, JSON

Scope and considerations

Designed for

  • End-to-end, Python-centric preprocessing and annotation of LC-MS metabolomics studies.

Not designed for

  • Universal vendor-raw support or confirmed metabolite identification from a mass/library annotation alone.

Important requirements

  • Python dependencies, centroid mzML data and a sequence CSV with sample names and paths; Thermo raw conversion additionally needs a configured converter and its runtime.

Considerations

  • Inspect sample-to-file mapping and QC/blank labels. Annotation depends on chosen empirical-compound rules and reference coverage, and downloaded resources are not automatically redistributable.

Access and documentation

Primary accessDocumentation

The LICENSE contains MIT terms preceded by wording that release under MIT is planned. Access is conservatively marked unclear pending clarification; downloaded annotation resources have separate licenses.

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. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. PLOS Computational Biology (2024). doi:10.1371/journal.pcbi.1011912

Related tools

MAWSimilar function · A reproducible metabolome-annotation workflow combining spectral-database dereplication and compound-database annotation from tandem mass-spectrometry data.MetaboAppsSimilar function · A modular collection of browser-based downstream analysis applications that extend GNPS2 results with targeted interpretation, statistics, metadata, and domain-specific readouts.MetabonautSimilar function · An open educational and reproducible workflow resource for script-based untargeted LC-MS/MS metabolomics using R and Bioconductor packages across preprocessing, QC, statistics, and annotation.msiFlowSimilar function · msiFlow provides Snakemake workflows for MSI preprocessing, segmentation, registration to immunofluorescence microscopy and region-based statistical or machine-learning analysis.

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