Boosting the Speed and Accuracy of Protein Quantification Algorithms in Mass Spectrometry-Based Proteomics
Thang V Pham1,2,3, Chau T M Tran3, Alex A Henneman1,2
1Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Medical Oncology, OncoProteomics Laboratory, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.
Journal of Proteome Research
|January 20, 2026
Summary
New algorithms and a data structure improve protein quantification in mass spectrometry. The iq format and optimized methods like maxlfq-bit offer significant speed and memory improvements for large datasets.
Area of Science:
- Proteomics
- Computational Biology
- Bioinformatics
Background:
- Protein quantification in mass spectrometry is essential for data analysis.
- Current methods, like MaxLFQ, struggle with large sample sizes due to memory and complexity limitations.
Purpose of the Study:
- To develop scalable and efficient algorithms for protein quantification in mass spectrometry.
- To introduce a novel data structure, the iq format, for handling large datasets.
Main Methods:
- Optimization of existing quantification methods for speed and memory efficiency.
- Development of new algorithms: maxlfq-bit and rlm-cd.
- Introduction of a generic weighting algorithm to reduce noise and improve accuracy.
Main Results:
- maxlfq-bit and rlm-cd achieve orders of magnitude speed improvements for large sample numbers.
- MaxLFQ demonstrates highest accuracy, despite higher computational cost.
- The weighting approach enhances the performance of all tested quantification methods.
Conclusions:
- The iq format and optimized algorithms significantly improve scalability and efficiency in protein quantification.
- Novel methods offer substantial speed gains for large-scale proteomics studies.
- The developed software is available in the R package iq.
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