Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics
ABSTRACT Metaproteomics offers a profound understanding of the functional dynamics of the gut microbiome, which is crucial for personalized healthcare strategies. The selection of an appropriate database is a critical step for the identification of peptides and proteins, as well as for the provision...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2025-02-01
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| Series: | Microbiology Spectrum |
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| Online Access: | https://journals.asm.org/doi/10.1128/spectrum.01755-24 |
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| author | Xiaowei Gao Hewei Liang Tongyuan Hu Yuanqiang Zou Liang Xiao |
| author_facet | Xiaowei Gao Hewei Liang Tongyuan Hu Yuanqiang Zou Liang Xiao |
| author_sort | Xiaowei Gao |
| collection | DOAJ |
| description | ABSTRACT Metaproteomics offers a profound understanding of the functional dynamics of the gut microbiome, which is crucial for personalized healthcare strategies. The selection of an appropriate database is a critical step for the identification of peptides and proteins, as well as for the provision of accurate taxonomic and functional annotations. The matched metagenomic-derived database is considered to be the best, but its limitations include the identification of low-abundance organisms and taxonomic resolution. Herein, we constructed a protein database (DBCGR2) based on Cultivated Genome Reference 2 (CGR2) and developed a complete peptide-centric analysis workflow for database searching and for the annotation of taxonomy and function. This workflow was subsequently appraised in comparison with metagenomics-derived databases for the analysis of metaproteomic data. Our findings suggested that the performance of DBCGR2 in identification was comparable with metagenomics-derived databases with improvement in identification rates of peptides from low-abundance species. The database searching results could be fully annotated using the pepTaxa taxonomic annotation approach developed in this study, and the taxonomic resolution was enhanced to strain level. Additionally, the results demonstrated that the sensitivity of functional annotation could be enhanced by employing DBCGR2. Overall, the DBCGR2 combined with pepTaxa can be considered an alternative for metaproteomic data analysis with superior analysis performances.IMPORTANCEMass spectrometry-based metaproteomics offers a profound understanding of the gut microbial taxonomy and functionality. The databases utilized in the analysis of metaproteomic data are crucial, as they determine the identification of proteins that can be recognized and linked to overall human health, in addition to the quality of taxonomic and functional annotation. Among the most effective approaches for constructing protein databases is the utilization of metagenomic sequencing to create matched databases. However, the database, derived from isolated genomes, has yet to undergo rigorous testing for their efficacy and accuracy in protein identification and taxonomic and functional annotation. Here, we constructed a protein database DBCGR2 derived from Cultivated Genome Reference 2 (CGR2) and a complete workflow for data analysis. We compared the performances of DBCGR2 and metagenomics-derived databases. Our results indicated that DBCGR2 can be regarded as an alternative to metagenomics-derived databases, which contribute to metaproteomic data analysis. |
| format | Article |
| id | doaj-art-76e1605b084e4082b43d3044856e8a43 |
| institution | Kabale University |
| issn | 2165-0497 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | Microbiology Spectrum |
| spelling | doaj-art-76e1605b084e4082b43d3044856e8a432025-02-04T14:03:40ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972025-02-0113210.1128/spectrum.01755-24Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomicsXiaowei Gao0Hewei Liang1Tongyuan Hu2Yuanqiang Zou3Liang Xiao4BGI Research, Shenzhen, ChinaBGI Research, Wuhan, ChinaBGI Research, Wuhan, ChinaBGI Research, Shenzhen, ChinaBGI Research, Shenzhen, ChinaABSTRACT Metaproteomics offers a profound understanding of the functional dynamics of the gut microbiome, which is crucial for personalized healthcare strategies. The selection of an appropriate database is a critical step for the identification of peptides and proteins, as well as for the provision of accurate taxonomic and functional annotations. The matched metagenomic-derived database is considered to be the best, but its limitations include the identification of low-abundance organisms and taxonomic resolution. Herein, we constructed a protein database (DBCGR2) based on Cultivated Genome Reference 2 (CGR2) and developed a complete peptide-centric analysis workflow for database searching and for the annotation of taxonomy and function. This workflow was subsequently appraised in comparison with metagenomics-derived databases for the analysis of metaproteomic data. Our findings suggested that the performance of DBCGR2 in identification was comparable with metagenomics-derived databases with improvement in identification rates of peptides from low-abundance species. The database searching results could be fully annotated using the pepTaxa taxonomic annotation approach developed in this study, and the taxonomic resolution was enhanced to strain level. Additionally, the results demonstrated that the sensitivity of functional annotation could be enhanced by employing DBCGR2. Overall, the DBCGR2 combined with pepTaxa can be considered an alternative for metaproteomic data analysis with superior analysis performances.IMPORTANCEMass spectrometry-based metaproteomics offers a profound understanding of the gut microbial taxonomy and functionality. The databases utilized in the analysis of metaproteomic data are crucial, as they determine the identification of proteins that can be recognized and linked to overall human health, in addition to the quality of taxonomic and functional annotation. Among the most effective approaches for constructing protein databases is the utilization of metagenomic sequencing to create matched databases. However, the database, derived from isolated genomes, has yet to undergo rigorous testing for their efficacy and accuracy in protein identification and taxonomic and functional annotation. Here, we constructed a protein database DBCGR2 derived from Cultivated Genome Reference 2 (CGR2) and a complete workflow for data analysis. We compared the performances of DBCGR2 and metagenomics-derived databases. Our results indicated that DBCGR2 can be regarded as an alternative to metagenomics-derived databases, which contribute to metaproteomic data analysis.https://journals.asm.org/doi/10.1128/spectrum.01755-24metaproteomicsprotein databasetaxonomic analysisfunctional analysis |
| spellingShingle | Xiaowei Gao Hewei Liang Tongyuan Hu Yuanqiang Zou Liang Xiao Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics Microbiology Spectrum metaproteomics protein database taxonomic analysis functional analysis |
| title | Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics |
| title_full | Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics |
| title_fullStr | Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics |
| title_full_unstemmed | Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics |
| title_short | Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics |
| title_sort | cultivated genome references for protein database construction and high resolution taxonomic annotation in metaproteomics |
| topic | metaproteomics protein database taxonomic analysis functional analysis |
| url | https://journals.asm.org/doi/10.1128/spectrum.01755-24 |
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