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MetaLab: an automated pipeline for metaproteomic data analysis
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712144/ https://www.ncbi.nlm.nih.gov/pubmed/29197424 http://dx.doi.org/10.1186/s40168-017-0375-2 |
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author | Cheng, Kai Ning, Zhibin Zhang, Xu Li, Leyuan Liao, Bo Mayne, Janice Stintzi, Alain Figeys, Daniel |
author_facet | Cheng, Kai Ning, Zhibin Zhang, Xu Li, Leyuan Liao, Bo Mayne, Janice Stintzi, Alain Figeys, Daniel |
author_sort | Cheng, Kai |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-017-0375-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5712144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57121442017-12-06 MetaLab: an automated pipeline for metaproteomic data analysis Cheng, Kai Ning, Zhibin Zhang, Xu Li, Leyuan Liao, Bo Mayne, Janice Stintzi, Alain Figeys, Daniel Microbiome Software 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-017-0375-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-02 /pmc/articles/PMC5712144/ /pubmed/29197424 http://dx.doi.org/10.1186/s40168-017-0375-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Cheng, Kai Ning, Zhibin Zhang, Xu Li, Leyuan Liao, Bo Mayne, Janice Stintzi, Alain Figeys, Daniel MetaLab: an automated pipeline for metaproteomic data 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 | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712144/ https://www.ncbi.nlm.nih.gov/pubmed/29197424 http://dx.doi.org/10.1186/s40168-017-0375-2 |
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