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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS

Peng Ran1, Yunzhi Wang1, Kai Li1

  • 1Center for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.

Briefings in Bioinformatics
|November 6, 2024
PubMed
Summary
This summary is machine-generated.

STAVER is a new algorithm that improves protein quantification precision in large-scale data-independent acquisition mass spectrometry (DIA-MS) analyses. It reduces noise, enhancing accuracy and reproducibility for reliable clinical research findings.

Keywords:
STAVER algorithmbioinformaticsdata-independent acquisitionnon-biological noiseproteomics analysisquantitative proteomics

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Mass spectrometry (MS)-based proteomics is crucial for biological system investigation.
  • Data-independent acquisition (DIA)-MS enhances protein identification and quantification.
  • Low-quality MS profiles compromise quantitative precision.

Purpose of the Study:

  • Introduce STAVER, a novel algorithm to reduce non-biological variation in large-scale DIA-MS.
  • Improve protein quantification precision and reliability in complex proteomic datasets.
  • Facilitate cross-platform and cross-laboratory comparative analyses.

Main Methods:

  • Developed STAVER algorithm utilizing standardized benchmark datasets.
  • Applied STAVER to reduce noise in MS signals for large-scale DIA-MS analyses.
  • Validated STAVER's performance across multiple DIA datasets.

Main Results:

  • STAVER significantly improved protein quantification precision, particularly in hybrid spectral library searches.
  • Demonstrated enhanced precision and reproducibility of protein quantification.
  • Validated STAVER's robustness and applicability in large-scale DIA proteomic data.

Conclusions:

  • STAVER offers an effective approach to enhance the quality of large-scale DIA proteomic data.
  • The algorithm improves consistency and reliability for clinical research.
  • STAVER facilitates more robust cross-platform and cross-laboratory comparisons.