1
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Wittig A, Miranda F, Hölzer M, Altenburg T, Bartoszewicz JM, Beyvers S, Dieckmann MA, Genske U, Giese SH, Nowicka M, Richard H, Schiebenhoefer H, Schmachtenberg AJ, Sieben P, Tang M, Tembrockhaus J, Renard BY, Fuchs S. CovRadar: continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance. Bioinformatics 2022; 38:4223-4225. [PMID: 35799354 DOI: 10.1093/bioinformatics/btac411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/13/2022] [Accepted: 06/13/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast. AVAILABILITY AND IMPLEMENTATION CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alice Wittig
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany.,Methods Development, Research Infrastructure and Information Technology (MFI), Bioinformatics and Systems Biology, Robert Koch Institute, Berlin, Germany
| | - Fábio Miranda
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Martin Hölzer
- Methods Development, Research Infrastructure and Information Technology (MFI), Bioinformatics and Systems Biology, Robert Koch Institute, Berlin, Germany
| | - Tom Altenburg
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Jakub M Bartoszewicz
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany.,Methods Development, Research Infrastructure and Information Technology (MFI), Bioinformatics and Systems Biology, Robert Koch Institute, Berlin, Germany
| | - Sebastian Beyvers
- Department of Biology and Chemistry, Justus-Liebig-University Gießen, Gießen 35390, Germany
| | - Marius A Dieckmann
- Department of Biology and Chemistry, Justus-Liebig-University Gießen, Gießen 35390, Germany
| | - Ulrich Genske
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Sven H Giese
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Melania Nowicka
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Hugues Richard
- Methods Development, Research Infrastructure and Information Technology (MFI), Bioinformatics and Systems Biology, Robert Koch Institute, Berlin, Germany
| | - Henning Schiebenhoefer
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | | | - Paul Sieben
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Ming Tang
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany.,Department of Human Genetics, Hannover Medical School, Hannover 30625, Germany
| | - Julius Tembrockhaus
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Bernhard Y Renard
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, Potsdam 14482, Germany
| | - Stephan Fuchs
- Methods Development, Research Infrastructure and Information Technology (MFI), Bioinformatics and Systems Biology, Robert Koch Institute, Berlin, Germany
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2
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 PMCID: PMC8674281 DOI: 10.1038/s41467-021-27542-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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3
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 DOI: 10.1101/2021.03.05.433915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 05/21/2023] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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4
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Verschaffelt P, Van Den Bossche T, Gabriel W, Burdukiewicz M, Soggiu A, Martens L, Renard BY, Schiebenhoefer H, Mesuere B. MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets. J Proteome Res 2021; 20:2083-2088. [PMID: 33661648 DOI: 10.1021/acs.jproteome.0c00926] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.
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Affiliation(s)
- Pieter Verschaffelt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent 9000, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Wassim Gabriel
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Michał Burdukiewicz
- Laboratory of Mass Spectrometry, Institute of Biochemistry and Biophysics Polish Academy of Sciences, Warsaw 02-106, Poland
| | - Alessio Soggiu
- "One Health" Section, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Bernhard Y Renard
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam 14482, Germany.,Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Henning Schiebenhoefer
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam 14482, Germany.,Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Bart Mesuere
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent 9000, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
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5
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Sajulga R, Easterly C, Riffle M, Mesuere B, Muth T, Mehta S, Kumar P, Johnson J, Gruening BA, Schiebenhoefer H, Kolmeder CA, Fuchs S, Nunn BL, Rudney J, Griffin TJ, Jagtap PD. Survey of metaproteomics software tools for functional microbiome analysis. PLoS One 2020; 15:e0241503. [PMID: 33170893 PMCID: PMC7654790 DOI: 10.1371/journal.pone.0241503] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 10/15/2020] [Indexed: 11/23/2022] Open
Abstract
To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.
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Affiliation(s)
- Ray Sajulga
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Caleb Easterly
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Michael Riffle
- University of Washington, Seattle, Washington, United States of America
| | | | - Thilo Muth
- Federal Institute for Materials Research and Testing, Berlin, Germany
| | - Subina Mehta
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Praveen Kumar
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - James Johnson
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | | | | | | | | | - Brook L. Nunn
- University of Washington, Seattle, Washington, United States of America
| | - Joel Rudney
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Timothy J. Griffin
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Pratik D. Jagtap
- University of Minnesota, Minneapolis, Minnesota, United States of America
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Schiebenhoefer H, Schallert K, Renard BY, Trappe K, Schmid E, Benndorf D, Riedel K, Muth T, Fuchs S. A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane. Nat Protoc 2020; 15:3212-3239. [PMID: 32859984 DOI: 10.1038/s41596-020-0368-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/29/2020] [Indexed: 12/14/2022]
Abstract
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
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Affiliation(s)
- Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kay Schallert
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Emanuel Schmid
- ID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Katharina Riedel
- Center for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
| | - Stephan Fuchs
- Department of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany.
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Schiebenhoefer H, Van Den Bossche T, Fuchs S, Renard BY, Muth T, Martens L. Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Rev Proteomics 2019; 16:375-390. [PMID: 31002542 DOI: 10.1080/14789450.2019.1609944] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.
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Affiliation(s)
- Henning Schiebenhoefer
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Tim Van Den Bossche
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
| | - Stephan Fuchs
- d FG13 Division of Nosocomial Pathogens and Antibiotic Resistances , Robert Koch Institute , Wernigerode , Germany
| | - Bernhard Y Renard
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Thilo Muth
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Lennart Martens
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
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