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Technical Note

Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform

1
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
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Department of Chemistry, University of Wisconsin, Madison, WI 53706, USA
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VIB-UGent Center for Medical Biotechnology, VIB, Ghent University, 9000 Ghent, Belgium
4
Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
*
Authors to whom correspondence should be addressed.
Proteomes 2020, 8(3), 15; https://doi.org/10.3390/proteomes8030015
Received: 12 June 2020 / Revised: 6 July 2020 / Accepted: 7 July 2020 / Published: 8 July 2020
For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform. View Full-Text
Keywords: proteomics; label-free quantification; galaxy framework; workflows proteomics; label-free quantification; galaxy framework; workflows
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MDPI and ACS Style

Mehta, S.; Easterly, C.W.; Sajulga, R.; Millikin, R.J.; Argentini, A.; Eguinoa, I.; Martens, L.; Shortreed, M.R.; Smith, L.M.; McGowan, T.; Kumar, P.; Johnson, J.E.; Griffin, T.J.; Jagtap, P.D. Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform. Proteomes 2020, 8, 15. https://doi.org/10.3390/proteomes8030015

AMA Style

Mehta S, Easterly CW, Sajulga R, Millikin RJ, Argentini A, Eguinoa I, Martens L, Shortreed MR, Smith LM, McGowan T, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform. Proteomes. 2020; 8(3):15. https://doi.org/10.3390/proteomes8030015

Chicago/Turabian Style

Mehta, Subina, Caleb W. Easterly, Ray Sajulga, Robert J. Millikin, Andrea Argentini, Ignacio Eguinoa, Lennart Martens, Michael R. Shortreed, Lloyd M. Smith, Thomas McGowan, Praveen Kumar, James E. Johnson, Timothy J. Griffin, and Pratik D. Jagtap 2020. "Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform" Proteomes 8, no. 3: 15. https://doi.org/10.3390/proteomes8030015

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