MetaDIA: A DDA-free Database Reduction Strategy for DIA Human Gut Metaproteomics
Haonan Duan1,2, Zhibin Ning2, Zhongzhi Sun2
1Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Nanjing 210004, China.
Genomics, Proteomics & Bioinformatics
|April 5, 2026
Summary
This study introduces MetaDIA, a new method for analyzing gut microbiome data. MetaDIA improves the speed and accuracy of identifying peptides in metaproteomics by reducing the search space.
Area of Science:
- Microbiology
- Metaproteomics
- Bioinformatics
Background:
- Gut microbiomes are complex, posing challenges for peptide identification in metaproteomics.
- Current data-independent acquisition (DIA) analysis relies on data-dependent acquisition (DDA) libraries, which are resource-intensive and limiting.
Purpose of the Study:
- To develop a novel strategy for reducing the search space in metaproteomics.
- To create an efficient workflow for analyzing DIA microbiome data without DDA assistance.
Main Methods:
- Utilized species and functional abundance information to score and prioritize peptides.
- Developed the MetaDIA workflow for DIA metaproteomics data analysis.
- Created a reduced, sufficient database for DIA data searching.
Main Results:
- MetaDIA successfully reduced the search space for metaproteomics.
- The workflow demonstrated strong consistency with traditional DDA-based library approaches.
- Achieved comparable protein and functional level identifications.
Conclusions:
- MetaDIA offers an efficient, DDA-independent approach for metaproteomics.
- This method enhances the application of DIA in microbiome research.
- MetaDIA is available as an open-source project.
