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Quality control and filtering

NeatMS

An open-source Python package that uses neural-network classification to label and filter false-positive MS1 peaks from untargeted LC-MS feature-detection workflows.

LC-MSUntargeted metabolomicsPython packageOpen source
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Overview

NeatMS labels and filters detected LC-MS MS1 signals using a neural-network classifier of peak quality. It is intended to follow upstream feature detection and uses raw chromatographic signal information together with the reported features.

What can this tool do?

Feature selection

Common uses

  • Remove low-quality or false-positive LC-MS features after peak detection.
  • Apply pretrained or user-trained neural-network classifiers to chromatographic signals.

Scientific fit

Primary function

Quality control and filtering

Other functions

Machine learning and prediction

Analysis types

Untargeted metabolomics

Data compatibility

Inputs

Feature or peak table, Raw instrument data

Outputs

Feature or peak table

Input formats

Feature-detection results and chromatographic data via package objects

Output formats

Python feature objects with predicted peak-quality labels for filtering

Scope and considerations

Designed for

  • Automated LC-MS signal-quality classification and filtering after feature detection.

Not designed for

  • Initial feature extraction, molecular annotation or a guarantee that every rejected low-quality signal is chemically irrelevant.

Important requirements

  • A compatible Python environment, detected feature information, corresponding raw LC-MS data and an appropriate supplied or trained classification model; follow the package examples for the expected import structure.

Considerations

  • Check compatibility with the older environment and upstream export format. Classifier suitability depends on peak shapes and acquisition conditions.

Access and documentation

Primary accessDocumentation

MIT code; the official repository is archived. Its documentation still says publication pending, while the subsequently published primary paper is cited here.

License: MIT

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. Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics. Analytical Chemistry (2022). doi:10.1021/acs.analchem.1c02220

Related tools

dbnormSimilar function · Compare statistical batch-correction models and inspect technical variation in metabolomics matrices.InjectionDesignSimilar function · A web service for designing LC-MS and GC-MS multi-omics injection sequences with QC placement, inter-batch balancing, intra-batch randomization, and downloadable worksheets.IonToolPackSimilar function · IonToolPack groups graphical mass-spectrometry utilities for raw-data visualization, PeakQC quality assessment, tandem-library matching, targeted MS1 extraction and feature-list comparison.ISFragSimilar function · ISFrag recognizes candidate in-source fragments in LC-MS data by combining feature relationships and available MS/MS evidence.

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