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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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Porcheddu M, Abbondio M, De Diego L, Uzzau S, Tanca A. Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations. J Proteome Res 2023. [PMID: 37116187 DOI: 10.1021/acs.jproteome.2c00803] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 04/30/2023]
Abstract
We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.
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Affiliation(s)
- Massimo Porcheddu
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Marcello Abbondio
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Laura De Diego
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
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Sethupathy S, Morales GM, Li Y, Wang Y, Jiang J, Sun J, Zhu D. Harnessing microbial wealth for lignocellulose biomass valorization through secretomics: a review. Biotechnol Biofuels 2021; 14:154. [PMID: 34225772 PMCID: PMC8256616 DOI: 10.1186/s13068-021-02006-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/26/2021] [Indexed: 05/10/2023]
Abstract
The recalcitrance of lignocellulosic biomass is a major constraint to its high-value use at industrial scale. In nature, microbes play a crucial role in biomass degradation, nutrient recycling and ecosystem functioning. Therefore, the use of microbes is an attractive way to transform biomass to produce clean energy and high-value compounds. The microbial degradation of lignocelluloses is a complex process which is dependent upon multiple secreted enzymes and their synergistic activities. The availability of the cutting edge proteomics and highly sensitive mass spectrometry tools make possible for researchers to probe the secretome of microbes and microbial consortia grown on different lignocelluloses for the identification of hydrolytic enzymes of industrial interest and their substrate-dependent expression. This review summarizes the role of secretomics in identifying enzymes involved in lignocelluloses deconstruction, the development of enzyme cocktails and the construction of synthetic microbial consortia for biomass valorization, providing our perspectives to address the current challenges.
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Affiliation(s)
- Sivasamy Sethupathy
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Gabriel Murillo Morales
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yixuan Li
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yongli Wang
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Jianxiong Jiang
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Jianzhong Sun
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Daochen Zhu
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2021] [Indexed: 12/13/2022] Open
Abstract
The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 12/13/2022] Open
Abstract
The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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