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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] [What about the content of this article? (0)] [Affiliation(s)] [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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