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Metabolite annotation and identification

NetID

An R-based global network-optimization method for annotating untargeted LC-MS features using measured masses, retention times, known chemical relationships, and optional MS/MS evidence.

LC-MSUntargeted metabolomicsR packageOpen source
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Overview

NetID globally annotates LC-MS features using a network of mass differences representing isotopes, adducts, fragments and biochemical transformations. Reference compounds and optional retention-time or tandem-spectral evidence constrain the optimization.

What can this tool do?

Adduct and isotope groupingSpectral-library matchingStructure-database searching

Common uses

  • Improve annotation coverage by optimizing assignments across a connected LC-MS feature network.
  • Use adduct, isotope, fragmentation, and biochemical-transformation relationships to constrain candidate assignments.

Scientific fit

Primary function

Metabolite annotation and identification

Other functions

Molecular networking and chemical similarity

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 not required

Data compatibility

Inputs

Raw instrument data, Feature or peak table, Mass spectra, Spectral library

Outputs

Compound list or annotations, Feature or peak table

Input formats

mzXML, CSV or TSV, MGF

Output formats

R annotation/network objects and feature assignments for downstream inspection

Scope and considerations

Designed for

  • Global annotation of untargeted LC-MS feature tables using network constraints.

Not designed for

  • Unconstrained de novo structure elucidation or experimental confirmation of every network-propagated annotation.

Important requirements

  • R environment, mzXML data, CSV peak and transformation tables, and an RDS reference-compound library; optional MS/MS matching also needs MGF spectra and an RDS spectral library.

Considerations

  • Candidate coverage and transformation rules constrain the network. Incorrect seeds or feature relationships can influence multiple assignments.

Access and documentation

Primary accessDocumentation

GPL-3.0 source distribution with example inputs and workflow guidance. User-supplied libraries and reference data have their own usage terms.

License: GPL-3.0

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. Metabolite discovery through global annotation of untargeted metabolomics data. Nature Methods (2021). doi:10.1038/s41592-021-01303-3

Related tools

BAMSimilar function · BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures.BUDDY / msbuddySimilar function · Generate and rank molecular formula annotations for small-molecule mass-spectrometry queries.CANOPUSSimilar function · A SIRIUS-integrated machine-learning method and webservice for predicting compound classes directly from high-resolution MS/MS data without requiring an exact database structure match.CFM-IDSimilar function · Predict tandem mass spectra, annotate fragments and rank candidate structures for small molecules.

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