Representative proteomes: a stable, scalable and unbiased proteome set for sequence analysis and functional
Chuming Chen1, Darren A Natale, Robert D Finn
1Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America.
Plos One
|May 11, 2011
Summary
We developed Representative Proteomes (RPs) to reduce protein sequence data by over 80% while preserving essential information. This approach aids computational analysis and protein characterization, offering diverse granularity options.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Exponential growth in protein sequences challenges computational and manual analysis resources.
- Minimizing data while retaining information is crucial for efficient biological data handling.
Purpose of the Study:
- To develop a method for reducing the size of proteomic datasets.
- To create a curated set of Representative Proteomes (RPs) that capture maximal information from similar proteomes.
- To provide RPs at various granularity levels for flexible data analysis.
Main Methods:
- Proteomes were clustered into Representative Proteome Groups (RPGs) based on UniRef50 co-membership.
- Representative Proteomes (RPs) were selected to best represent their respective RPGs.
- RPs were generated using different co-membership thresholds (CMT): 75%, 55%, 35%, and 15%.
Main Results:
- A 55% co-membership threshold (RP55) demonstrated strong alignment with standard taxonomic classifications.
- The RP set reduced sequence space by over 80% compared to UniProtKB.
- Sequence diversity (over 95% of InterPro domains) and annotation information (93% of experimentally characterized proteins) were retained.
Conclusions:
- Representative Proteomes offer a significant reduction in data size while maintaining high information content.
- RPs facilitate efficient sequence similarity searches, protein classification, and targeted characterization.
- The RP dataset provides a valuable resource for managing and analyzing large-scale proteomic data.
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