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Updated: May 29, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Threshold-avoiding proteomics pipeline.

Frank Suits1, Berend Hoekman, Therese Rosenling

  • 1IBM T. J. Watson Research Center, P.O. Box 218, Yorktown Heights, New York 10598, USA. suits@us.ibm.com

Analytical Chemistry
|September 2, 2011
PubMed
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This summary is machine-generated.

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We developed a new proteomics analysis pipeline, TAPP (threshold-avoiding proteomics pipeline), to enhance detection of low-abundance molecules in LC-MS data. This method improves quantification and identifies biologically relevant compounds by minimizing early data culling.

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) is crucial for proteomics.
  • Existing LC-MS data analysis methods face challenges in maximizing dynamic range and quantifying low-abundance peaks.
  • Distinguishing true biological signals from noise in complex LC-MS data remains a significant hurdle.

Purpose of the Study:

  • To develop a novel proteomics analysis pipeline to maximize dynamic range in LC-MS data.
  • To accurately quantify low-abundance peaks for identifying biologically relevant molecules.
  • To improve the signal-to-noise ratio by delaying noise peak removal.

Main Methods:

  • Developed a threshold-avoiding proteomics pipeline (TAPP).
  • Incorporated novel preprocessing, peak detection, time alignment, and cluster-based matching algorithms.

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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  • Focused on quantitative low-level processing of raw LC-MS data.
  • Utilized coherent behavior of peaks across multiple datasets to distinguish signal from noise.

    • Main Results:

    • TAPP effectively maximizes the dynamic range of detected molecules in LC-MS data.
    • The pipeline accurately quantifies low-abundance peaks, enhancing biological relevance identification.
    • Demonstrated performance on porcine cerebrospinal fluid spiked with varying concentrations of horse heart cytochrome c.

    Conclusions:

    • TAPP offers a robust approach for enhanced proteomics analysis using LC-MS data.
    • The threshold-avoiding strategy successfully distinguishes low-abundance signals from noise.
    • This pipeline improves the sensitivity and accuracy of quantitative proteomics.