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 (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.

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Precursor intensity-based label-free quantification software tools for proteomic and multiomic analysis within the Galaxy Platform

10.1101/2020.04.01.003988 ◽

2020 ◽

Author(s):

Subina Mehta ◽

Caleb Easterly ◽

Ray Sajulga ◽

Robert J. Millikin ◽

Andrea Argentini ◽

...

Keyword(s):

Dynamic Range ◽

Protein Quantification ◽

Label Free ◽

Label Free Quantification ◽

File Formats ◽

Rigorous Testing ◽

The Galaxy ◽

Level Information ◽

Easy Integration ◽

Free Quantification

AbstractFor 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. In LFQ workflows, protein abundance changes are inferred from peptide-level information, including microbial peptides (for metaproteomics) and peptides carrying post-translational modifications (for proteomics) and/or variant sequences (for proteogenomics). Multi-omics studies (such as proteogenomics and metaproteomics) rely on peptide detection and quantification to identify and quantify peptides that map to unique proteoforms and metaproteins. The Galaxy for proteomics (Galaxy-P) platform has proven useful for the development of accessible workflows to identify proteins in these complex multi-omic studies. However, proteomics workflows within the Galaxy platform have lacked well-tested label-free quantification tools.In this study, our main goals were to evaluate two recently published open-source LFQ tools and to implement them within the Galaxy platform, enabling their easy integration with established workflows. These two tools, moFF and FlashLFQ, were selected based on their described peptide quantification capabilities and amenability to Galaxy implementation. Through rigorous testing and communication with the tools’ developers, we gained insights into the software features necessary for maximizing the performance of each tool. Software features evaluated included: a) match-between-runs (MBR); b) using both Thermo .raw and HUPO standards .mzML file formats as input for improved quantification; c) use of containers and/or conda packages; d) parameters needed for analyzing large input datasets; and e) optimization and validation of software performance. This work 1) establishes a process for software implementation, optimization and validation within Galaxy; and 2) makes powerful new tools for LFQ available which should prove highly useful for a variety of proteomics and multi-omics applications employing the Galaxy platform.

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In-depth evaluation of software tools for data-independent acquisition based label-free quantification

PROTEOMICS ◽

10.1002/pmic.201400396 ◽

2015 ◽

Vol 15 (18) ◽

pp. 3140-3151 ◽

Cited By ~ 26

Author(s):

Jörg Kuharev ◽

Pedro Navarro ◽

Ute Distler ◽

Olaf Jahn ◽

Stefan Tenzer

Keyword(s):

Software Tools ◽

Label Free ◽

Label Free Quantification ◽

Data Independent Acquisition ◽

Free Quantification

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Label-free quantification and post-translational modifications

Journal of Proteomics ◽

10.1016/j.jprot.2020.103962 ◽

2020 ◽

Vol 229 ◽

pp. 103962

Author(s):

Hugo Miguel Santos ◽

Carlos Lodeiro ◽

José Luis Capelo

Keyword(s):

Label Free ◽

Post Translational Modifications ◽

Label Free Quantification ◽

Free Quantification

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freeQuant: A Mass Spectrometry Label-Free Quantification Software Tool for Complex Proteome Analysis

The Scientific World JOURNAL ◽

10.1155/2015/137076 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ning Deng ◽

Zhenye Li ◽

Chao Pan ◽

Huilong Duan

Keyword(s):

Mass Spectrometry ◽

Functional Analysis ◽

Quantitative Analysis ◽

Protein Quantification ◽

Mouse Heart ◽

Label Free ◽

Spectral Count ◽

Label Free Quantification ◽

Mass Spectrometry Technology ◽

Free Quantification

Study of complex proteome brings forward higher request for the quantification method using mass spectrometry technology. In this paper, we present a mass spectrometry label-free quantification tool for complex proteomes, called freeQuant, which integrated quantification with functional analysis effectively. freeQuant consists of two well-integrated modules: label-free quantification and functional analysis with biomedical knowledge. freeQuant supports label-free quantitative analysis which makes full use of tandem mass spectrometry (MS/MS) spectral count, protein sequence length, shared peptides, and ion intensity. It adopts spectral count for quantitative analysis and builds a new method for shared peptides to accurately evaluate abundance of isoforms. For proteins with low abundance, MS/MS total ion count coupled with spectral count is included to ensure accurate protein quantification. Furthermore, freeQuant supports the large-scale functional annotations for complex proteomes. Mitochondrial proteomes from the mouse heart, the mouse liver, and the human heart were used to evaluate the usability and performance of freeQuant. The evaluation showed that the quantitative algorithms implemented in freeQuant can improve accuracy of quantification with better dynamic range.

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