MetaLab: an automated pipeline for metaproteomic data analysis
Abstract Background Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quan...
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | Microbiome |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40168-017-0375-2 |
| _version_ | 1818146460406906880 |
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| author | Kai Cheng Zhibin Ning Xu Zhang Leyuan Li Bo Liao Janice Mayne Alain Stintzi Daniel Figeys |
| author_facet | Kai Cheng Zhibin Ning Xu Zhang Leyuan Li Bo Liao Janice Mayne Alain Stintzi Daniel Figeys |
| author_sort | Kai Cheng |
| collection | DOAJ |
| description | Abstract Background Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification. Result Here, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples. Conclusion MetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies. |
| first_indexed | 2024-12-11T12:19:42Z |
| format | Article |
| id | doaj.art-d6649c1d2db84d39b8b3738954b2e28e |
| institution | Directory Open Access Journal |
| issn | 2049-2618 |
| language | English |
| last_indexed | 2024-12-11T12:19:42Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj.art-d6649c1d2db84d39b8b3738954b2e28e2022-12-22T01:07:34ZengBMCMicrobiome2049-26182017-12-015111010.1186/s40168-017-0375-2MetaLab: an automated pipeline for metaproteomic data analysisKai Cheng0Zhibin Ning1Xu Zhang2Leyuan Li3Bo Liao4Janice Mayne5Alain Stintzi6Daniel Figeys7Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaDepartment of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Faculty of Medicine, University of OttawaAbstract Background Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification. Result Here, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples. Conclusion MetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.http://link.springer.com/article/10.1186/s40168-017-0375-2MetaproteomicsProtein identificationQuantificationTaxonomy analysis |
| spellingShingle | Kai Cheng Zhibin Ning Xu Zhang Leyuan Li Bo Liao Janice Mayne Alain Stintzi Daniel Figeys MetaLab: an automated pipeline for metaproteomic data analysis Microbiome Metaproteomics Protein identification Quantification Taxonomy analysis |
| title | MetaLab: an automated pipeline for metaproteomic data analysis |
| title_full | MetaLab: an automated pipeline for metaproteomic data analysis |
| title_fullStr | MetaLab: an automated pipeline for metaproteomic data analysis |
| title_full_unstemmed | MetaLab: an automated pipeline for metaproteomic data analysis |
| title_short | MetaLab: an automated pipeline for metaproteomic data analysis |
| title_sort | metalab an automated pipeline for metaproteomic data analysis |
| topic | Metaproteomics Protein identification Quantification Taxonomy analysis |
| url | http://link.springer.com/article/10.1186/s40168-017-0375-2 |
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