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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass
Guoshou Teo1, Sinae Kim2, Chih-Chiang Tsou3
1Department of Applied Probability and Statistics, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Journal of Proteomics
|September 19, 2015
Summary
mapDIA is a new software package for analyzing data independent acquisition (DIA) mass spectrometry data. It provides statistical analysis of differential protein expression from fragment-level intensities, improving accuracy and false discovery rate control.
Area of Science:
- Proteomics
- Mass Spectrometry
- Computational Biology
Background:
- Data independent acquisition (DIA) mass spectrometry enables comprehensive peptide and protein detection and quantification across samples.
- DIA generates fragment-level quantification data, suitable for repeated measurements in statistical analysis.
- Limited statistical methods exist for aggregating DIA fragment-level data into peptide- or protein-level summaries.
Purpose of the Study:
- To introduce mapDIA, a software package for statistical analysis of differential protein expression using DIA fragment-level intensities.
- To provide a robust workflow for normalizing, selecting, and statistically analyzing DIA data.
- To enable accurate detection of differentially expressed proteins with controlled false discovery rates.
Main Methods:
- mapDIA employs a three-step workflow: intensity normalization, peptide/fragment selection, and statistical analysis.
- Normalization methods include total intensity sums and a novel local intensity sum approach in retention time space.
- Peptide/fragment selection removes outliers and identifies features preserving quantitative patterns, followed by model-based statistical significance analysis.
Main Results:
- mapDIA effectively detects differentially expressed proteins while maintaining accurate control of false discovery rates, as demonstrated by simulations.
- The software successfully analyzed two published DIA datasets: 14-3-3β dynamic interaction network and prostate cancer glycoproteome.
- The novel normalization and selection strategies contribute to improved statistical analysis of DIA data.
Conclusions:
- mapDIA offers a powerful and statistically sound approach for analyzing DIA mass spectrometry data.
- The software facilitates robust differential protein expression analysis from complex fragment-level intensity data.
- mapDIA is a valuable tool for researchers in proteomics and computational biology studying protein expression changes.
