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

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 (analyz...

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Bibliographic Details
Published in:Proteomes 2020-07, Vol.8 (3), p.15
Main Authors: Mehta, Subina, Easterly, Caleb W, Sajulga, Ray, Millikin, Robert J, Argentini, Andrea, Eguinoa, Ignacio, Martens, Lennart, Shortreed, Michael R, Smith, Lloyd M, McGowan, Thomas, Kumar, Praveen, Johnson, James E, Griffin, Timothy J, Jagtap, Pratik D
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Computer programs
Data analysis
Datasets
E coli
galaxy framework
label-free quantification
Mass spectroscopy
Open source software
Peptides
Post-translation
Proteins
Proteomics
Quantitation
Search engines
Software
Studies
Technical Note
workflows
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Summary: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.
ISSN:2227-7382
2227-7382
DOI:10.3390/proteomes8030015