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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] [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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Rajczewski AT, Jagtap PD, Griffin TJ. An overview of technologies for MS-based proteomics-centric multi-omics. Expert Rev Proteomics 2022; 19:165-181. [PMID: 35466851 PMCID: PMC9613604 DOI: 10.1080/14789450.2022.2070476] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems. AREAS COVERED Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics. EXPERT COMMENTARY Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Coauthor, Research Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
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Alcaraz AJG, Potěšil D, Mikulášek K, Green D, Park B, Burbridge C, Bluhm K, Soufan O, Lane T, Pipal M, Brinkmann M, Xia J, Zdráhal Z, Schneider D, Crump D, Basu N, Hogan N, Hecker M. Development of a Comprehensive Toxicity Pathway Model for 17α-Ethinylestradiol in Early Life Stage Fathead Minnows ( Pimephales promelas). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:5024-5036. [PMID: 33755441 DOI: 10.1021/acs.est.0c05942] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
There is increasing pressure to develop alternative ecotoxicological risk assessment approaches that do not rely on expensive, time-consuming, and ethically questionable live animal testing. This study aimed to develop a comprehensive early life stage toxicity pathway model for the exposure of fish to estrogenic chemicals that is rooted in mechanistic toxicology. Embryo-larval fathead minnows (FHM; Pimephales promelas) were exposed to graded concentrations of 17α-ethinylestradiol (water control, 0.01% DMSO, 4, 20, and 100 ng/L) for 32 days. Fish were assessed for transcriptomic and proteomic responses at 4 days post-hatch (dph), and for histological and apical end points at 28 dph. Molecular analyses revealed core responses that were indicative of observed apical outcomes, including biological processes resulting in overproduction of vitellogenin and impairment of visual development. Histological observations indicated accumulation of proteinaceous fluid in liver and kidney tissues, energy depletion, and delayed or suppressed gonad development. Additionally, fish in the 100 ng/L treatment group were smaller than controls. Integration of omics data improved the interpretation of perturbations in early life stage FHM, providing evidence of conservation of toxicity pathways across levels of biological organization. Overall, the mechanism-based embryo-larval FHM model showed promise as a replacement for standard adult live animal tests.
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Affiliation(s)
- Alper James G Alcaraz
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
| | - David Potěšil
- Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Kamil Mikulášek
- Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Derek Green
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
| | - Bradley Park
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
| | - Connor Burbridge
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Kerstin Bluhm
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
| | - Othman Soufan
- Computer Science Department, St. Francis Xavier University, Antigonish, Nova Scotia B2G 2W5, Canada
| | - Taylor Lane
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
- Department of Environment and Geography, York University, York YO10 5NG, United Kingdom
| | - Marek Pipal
- RECETOX, Masaryk University, Brno 625 00, Czech Republic
| | - Markus Brinkmann
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
- School of the Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5C8, Canada
- Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan S7N 3H5, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada
| | - Zbyněk Zdráhal
- Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - David Schneider
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, Saskatchewan S7N 0W9, Canada
- School of the Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5C8, Canada
| | - Doug Crump
- Environment and Climate Change Canada, National Wildlife Research Centre, Ottawa, Ontario K1A 0H3, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada
| | - Natacha Hogan
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
- Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A8, Canada
| | - Markus Hecker
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada
- School of the Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5C8, Canada
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Mehta S, Kumar P, Crane M, Johnson JE, Sajulga R, Nguyen DDA, McGowan T, Arntzen MØ, Griffin TJ, Jagtap PD. Updates on metaQuantome Software for Quantitative Metaproteomics. J Proteome Res 2021; 20:2130-2137. [PMID: 33683127 DOI: 10.1021/acs.jproteome.0c00960] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Marie Crane
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Dinh Duy An Nguyen
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås 1432, Norway
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
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McGowan T, Johnson JE, Kumar P, Sajulga R, Mehta S, Jagtap PD, Griffin TJ. Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration. Gigascience 2020; 9:giaa025. [PMID: 32236523 PMCID: PMC7102281 DOI: 10.1093/gigascience/giaa025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/13/2020] [Accepted: 02/24/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Proteogenomics integrates genomics, transcriptomics, and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing, and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FINDINGS MVP is built as an HTML Galaxy plug-in, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input-a custom data type (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. CONCLUSIONS MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.
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Affiliation(s)
- Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, 599 Walter Library, 117 Pleasant Street SE, Minneapolis, MN 55455, USA
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, 599 Walter Library, 117 Pleasant Street SE, Minneapolis, MN 55455, USA
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
- Bioinformatics and Computational Biology program, University of Minnesota-Rochester, 111 South Broadway, Suite 300, Rochester, MN 55904, USA
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
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