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Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)
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apLCMS--adaptive processing of high-resolution LC/MS data.

Tianwei Yu1, Youngja Park, Jennifer M Johnson

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu

Bioinformatics (Oxford, England)
|May 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms for processing high-resolution liquid chromatography-mass spectrometry (LC/MS) data, improving metabolite quantification in complex biological samples. The apLCMS R package offers efficient and accurate analysis, addressing key challenges in LC/MS data processing.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Liquid chromatography-mass spectrometry (LC/MS) profiling is crucial for metabolite quantification in complex biological samples.
  • Existing LC/MS data analysis faces challenges in noise reduction, feature identification, alignment, and computational efficiency.

Purpose of the Study:

  • To develop and present a set of advanced algorithms for processing high-resolution LC/MS data.
  • To enhance the accuracy and efficiency of metabolite quantification from complex biological samples.

Main Methods:

  • Development of algorithms featuring adaptive tolerance level searching, non-parametric intensity grouping, and run filters.
  • Implementation of model-based peak intensity estimation for absolute quantification.
  • Package development in R (apLCMS) for efficient processing of large LC/MS datasets.

Main Results:

  • The apLCMS package demonstrates efficient processing of large-scale LC/MS datasets.
  • The algorithms improve noise reduction, feature identification, and quantification accuracy.
  • Enhanced preservation of weak signals and accurate absolute quantification of metabolites are achieved.

Conclusions:

  • The apLCMS R package provides a robust and efficient solution for high-resolution LC/MS data analysis.
  • The developed algorithms address critical challenges in metabolomics data processing.
  • This approach facilitates more reliable metabolite quantification in biological research.