Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis
Jinqiu Xiao1, Alessandro Tanca2, Ben Jia1
1Department of Bioinformatics and Biostatistics , School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai 200240 , People's Republic of China.
Journal of Proteome Research
|February 14, 2018
Summary
A new metagenomic taxonomy-guided (MT) strategy improves metaproteomic analysis by merging public databases with metagenome assembly. This approach enhances peptide identification sensitivity and precision, especially for poorly characterized microbial species.
Area of Science:
- Microbiology
- Proteomics
- Bioinformatics
Background:
- Metaproteomics offers functional insights into microbial communities by analyzing all expressed proteins.
- Constructing comprehensive sequence databases for metaproteomics is challenging due to microbial community complexity and heterogeneity.
- Existing databases (public or metagenomic) often lack adequate coverage for identifying proteins from all microbial species, particularly poorly characterized ones.
Purpose of the Study:
- To develop a novel metagenomic taxonomy-guided database-search strategy (MT) to overcome limitations in metaproteomic database construction.
- To enhance the sensitivity and precision of peptide identification in metaproteomic studies.
- To enable the detection of proteins from underrepresented or poorly characterized microbial species.
Main Methods:
- Developed a metagenomic taxonomy-guided (MT) database-search strategy.
- Created a merged database combining taxonomy-guided reference protein sequences from public databases and proteins from metagenome assembly.
- Applied the MT strategy to a mock microbial mixture and a human gut microbial sample for validation.
Main Results:
- The MT strategy identified approximately twice as many peptides in a mock microbial mixture compared to using only a metagenomic database.
- Taxonomic attribution reliability using the MT strategy was comparable to using an a priori matched database, with similar misassignment rates.
- In a human gut sample, the MT strategy identified 1.7 times more peptides than a standard metagenomic database.
Conclusions:
- The MT strategy effectively constructs databases that significantly improve peptide identification in metaproteomics.
- This approach enhances both the sensitivity and precision of metaproteomic analyses.
- The MT strategy facilitates the detection of proteins from a wider range of microbial species, including those that are poorly characterized.
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