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Updated: May 9, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified

Damian Fermin1, Scott J Walmsley, Anne-Claude Gingras

  • 1Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109;

Molecular & Cellular Proteomics : MCP
|August 7, 2013
PubMed
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LuciPHOr improves phosphoproteomics by accurately estimating the false localization rate (FLR) for phosphorylation sites. This new computational approach enhances the reliability of identifying modified peptides in complex biological samples.

Area of Science:

  • Proteomics
  • Computational Biology
  • Biochemistry

Background:

  • Phosphorylation site localization is critical for phosphoproteomics but challenging due to complex peptide spectra.
  • Existing computational tools often lack direct false localization rate (FLR) estimation, limiting objective scoring.
  • Multiple phosphorylation sites on single peptides complicate accurate localization.

Purpose of the Study:

  • To develop a novel computational approach, LuciPHOr, for accurate phosphorylation site localization and FLR estimation.
  • To improve the reliability of phosphoproteomic data analysis, especially for multiphosphorylated peptides.
  • To provide a tool compatible with standard phosphoproteomic data processing pipelines.

Main Methods:

  • LuciPHOr employs a modified target-decoy strategy using mass accuracy and peak intensities for site scoring.

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Last Updated: May 9, 2026

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Published on: May 18, 2017

  • It performs simultaneous localization on all candidate sites within a peptide.
  • FLR is estimated by comparing non-decoy phosphopeptides against decoys with artificial phosphorylation sites.

    • Main Results:

    • LuciPHOr provides accurate FLR estimates and site-level confidence scores, enabling localization of additional sites.
    • The method demonstrated sensitivity and accuracy in evaluating synthetic and complex biological phosphoproteomic datasets.
    • LuciPHOr is compatible with Trans-Proteomic Pipeline outputs, enhancing its applicability.

    Conclusions:

    • LuciPHOr offers a robust solution for phosphorylation site localization and FLR estimation in large-scale phosphoproteomics.
    • The tool enhances the confidence and scope of phosphosite identification, particularly in complex samples like mouse brain.
    • LuciPHOr represents a significant advancement in computational phosphoproteomics analysis.