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RIPPER: a framework for MS1 only metabolomics and proteomics label-free relative quantification
Susan K Van Riper1, LeeAnn Higgins2, John V Carlis3
1Department of Biomedical Informatics and Computational Biology, University of Minnesota, Rochester University of Minnesota Informatics Institute, University of Minnesota, St Paul.
Bioinformatics (Oxford, England)
|May 7, 2016
Summary
RIPPER is a new framework for label-free relative quantification in proteomics and metabolomics. It integrates multiple algorithms and novel normalization for accurate analyte quantification in mass spectrometry (MS) studies.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Bioinformatics
Background:
- Label-free quantitative proteomics and metabolomics are crucial for biological discovery.
- Accurate quantification requires robust pre-processing, alignment, and normalization strategies.
- Existing software frameworks may lack integrated solutions for these challenges.
Purpose of the Study:
- To introduce RIPPER, a comprehensive software framework for mass-spectrometry-based label-free relative quantification.
- To integrate established algorithms for pre-processing, quantification, alignment, and grouping.
- To implement a novel proximity-based intensity normalization method.
Main Methods:
- RIPPER combines pre-existing algorithms for analyte quantification and retention time alignment.
- It incorporates analyte grouping across multiple runs.
- The framework introduces proximity-based intensity normalization for improved data processing.
Main Results:
- RIPPER provides lists of analyte signals with both unnormalized and normalized intensities.
- The framework facilitates the detection of quantitative differences between biological samples.
- It serves as input for statistical and directed mass spectrometry (MS) analysis.
Conclusions:
- RIPPER offers an integrated and robust solution for label-free quantitative proteomics and metabolomics.
- The novel normalization method enhances the accuracy of quantitative analysis.
- This framework supports advanced MS-based methods for biological sample comparison.
