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Preprocessing and feature extraction

Everything Bagel

Detect, align and gap-fill LC-MS features, with a GNPS2 workflow connecting raw LC-MS/MS data to feature-based molecular networking.

LC-MSWeb applicationFree web service
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Overview

Everything Bagel is a feature-finding resource for untargeted LC-MS analysis. Its 2026 preprint describes feature detection, retention-time alignment and gap filling. The GNPS2-hosted workflow combines feature finding with FBMN, making this an upstream processing route into subsequent network interpretation.

Also known as: Everything Bagel Feature Finder, Everything Bagel Workflow

What can this tool do?

Peak or feature detectionRetention-time alignmentGap filling

Common uses

  • Generate aligned quantitative features from LC-MS data.
  • Connect raw-data processing with GNPS2 feature-based molecular networking.

Scientific fit

Primary function

Preprocessing and feature extraction

Other functions

Molecular networking and chemical similarity

Data compatibility

Inputs

Raw instrument data

Outputs

Feature or peak table, Human-readable report

Input formats

LC-MS/MS files accepted by the GNPS2 workflow form

Output formats

Aligned feature tables and GNPS2 FBMN results

Scope and considerations

Designed for

  • Untargeted LC-MS feature finding and a raw-data-to-FBMN workflow.

Not designed for

  • A downstream statistical-analysis replacement or proof of compound identity.

Important requirements

  • A GNPS2 account and compatible LC-MS(/MS) data; check the workflow form for file and parameter requirements.

Considerations

  • The dedicated publication is a preprint, not a peer-reviewed validation.
  • Reported speed and benchmark performance are author claims and were not independently reproduced here.
  • It is an upstream workflow, not solely an analysis of a previously generated network.

Education

Learn this tool

External tutorials and workshops collected by Navigator. Entries marked Needs review have not been checked end to end.

NTMSS2026 Day3 S5 - Yourae Shin & Ming Wang - Feature Detection with Everything BagelFunctional Metabolomics Lab · Video Workshop · Needs review

Browse all education resources →

Access and documentation

Primary accessDocumentation

Hosted GNPS2 execution requires an account and permits academic/non-commercial use; commercial access requires a license. A standalone code repository and software license were not verified in this review.

Evidence and review

GNPS2 homepage announcement, hosted workflow link, FBMN input definition and Crossref preprint abstract/metadata checked on 2026-09-23. Preprint full text was not accessible; detailed performance, format and standalone software claims remain unverified. No analysis job was run.

Core publications

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

  1. Preprint · not peer reviewed Shin Y, El Abiead Y, Jarmusch AK, et al. The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics. bioRxiv (2026), preprint. doi:10.64898/2026.08.17.744735

    Preprint; not peer reviewed.

Related tools

GNPS2 Feature-Based Molecular Networking workflowIntegrates with · The GNPS2 workflow joins feature finding to FBMN; the FBMN input definition includes an Everything Bagel format option.GNPSPart of · Available as a hosted workflow in GNPS2.

Entry history

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

  1. 2026-09-23 · First committed entry

    Add ChemWalker and Everything Bagel and audit GNPS2 downstream coverage

    View change · Entry at this revision

Full GitHub history