METASPACE

METASPACE annotates metabolites in high-mass-resolution imaging mass spectrometry (IMS) datasets, providing FDR-controlled molecular sum formula identification and spatial localization within tissues, cell cultures, and agar plates at cellular resolution.


Key Features:

  • FDR-controlled annotation: Performs false discovery rate control for metabolite annotation at the molecular sum formula level.
  • Metabolite-signal match score: Uses a scoring system that quantifies the match between observed signals and known metabolites.
  • Target-decoy FDR estimation: Implements a target-decoy approach to estimate FDR specifically for spatial metabolomics data.
  • Automated metabolite identification: Automates identification and annotation of hundreds of metabolites in IMS datasets.
  • Cellular-resolution spatial localization: Localizes metabolites within tissues, cell cultures, and agar plates at cellular resolution.
  • Cloud-based computational engine: Provides scalable computational resources for processing high-mass-resolution IMS data.

Scientific Applications:

  • Spatial metabolomics: Enables mapping of metabolite distributions in tissues, cell cultures, and agar plates to investigate cellular processes and disease mechanisms.
  • Imaging mass spectrometry analysis: Supports annotation and interpretation of high-mass-resolution IMS datasets for molecular localization studies.

Methodology:

Performs FDR-controlled annotation at the molecular sum formula level using a metabolite-signal match score combined with target-decoy FDR estimation.

Topics

Details

License:
Apache-2.0
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python, JavaScript, Shell, Other
Added:
11/20/2021
Last Updated:
11/24/2024

Operations

Publications

Palmer A, Phapale P, Chernyavsky I, Lavigne R, Fay D, Tarasov A, Kovalev V, Fuchser J, Nikolenko S, Pineau C, Becker M, Alexandrov T. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nature Methods. 2016;14(1):57-60. doi:10.1038/nmeth.4072. PMID:27842059.

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