Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Preprocessing and feature extraction

MassCube

An integrated Python package for LC-MS data processing with nontargeted peak detection and segmentation, feature quality evaluation, feature-group annotation, and MS/MS library or analog searching.

LC-MSUntargeted metabolomicsPython packageCommand lineOpen source
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Overview

MassCube processes LC-MS data into aligned feature tables and supports feature-quality evaluation, ion grouping and spectral-library annotation. Its command-line workflow combines raw files, sample information and parameters; available normalization and annotation steps depend on the supplied metadata and reference libraries.

What can this tool do?

Chromatogram buildingPeak or feature detectionGap fillingAdduct and isotope groupingSpectral-library matchingMS/MS processing

Common uses

  • Process LC-MS data from raw spectra to aligned quantitative features.
  • Evaluate feature quality and group isotopes, adducts, and in-source fragments.
  • Search MS/MS spectra against identity or analog references.

Scientific fit

Primary function

Preprocessing and feature extraction

Other functions

Quality control and filtering, Metabolite annotation and identification, Spectral processing and comparison

Analysis types

Untargeted metabolomics

MS level

MS1, MS/MS

Acquisition

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

Ion mobility

Ion mobility supported

Data compatibility

Inputs

Raw instrument data, Mass spectra

Outputs

Feature or peak table, Mass spectra, Compound list or annotations

Input formats

mzML, mzXML, Vendor raw format

Output formats

CSV or TSV, MSP

Scope and considerations

Designed for

  • Integrated Python-based LC-MS preprocessing and feature-level annotation.

Not designed for

  • Universal vendor-file conversion or confirming molecular structures from feature grouping or analog matches.

Important requirements

  • Python, mzML or mzXML data, and workflow parameters. Supply sample/QC metadata for normalization and statistics and a compatible MS/MS library for spectral annotation.

Considerations

  • The native Bruker reader reports inverse mobility without proprietary per-frame pressure compensation. Keep original acquisition files even when using workflow caches.

Access and documentation

Primary accessHomepageSource repository

Apache-2.0 source distribution. Native Bruker reading is restricted to documented TDF2/DDA-PASEF and calibration variants; it is not general support for every Bruker acquisition.

License: Apache-2.0

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. MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers. Nature Communications (2025). doi:10.1038/s41467-025-60640-5

Related tools

asariSimilar function · Process high-resolution mass-spectrometry data into aligned feature tables.AutoCCSSimilar function · An open-source Python tool for calculating and calibrating experimental collision cross sections from ion-mobility mass-spectrometry data across drift-tube and traveling-wave workflows.BreathXplorerSimilar function · breathXplorer processes high-resolution breath mass-spectrometry data with feature extraction, alignment and breath-recognition functions.CAMERASimilar function · Group and annotate related LC-MS signals after XCMS peak detection.

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