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

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
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.

Label-Free Quantification by Data Independent Acquisition Mass Spectrometry to Map Cardiovascular Proteomes

2016 ◽  
pp. 227-245
Author(s):  
Sarah J. Parker ◽  
Ronald J. Holewinski ◽  
Irina Tchernyshyov ◽  
Vidya Venkatraman ◽  
Laurie Parker ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Label Free ◽  
Label Free Quantification ◽  
Data Independent Acquisition ◽  
Free Quantification

scholarly journals Host defense responses of CO441 and CL30 maize lines to Fusarium graminearum analyzed by comparative label-free quantitative proteomics

2019 ◽  
Author(s):  
Lana M. Reid ◽  
Illimar Altosaar
Keyword(s):  
Mass Spectrometry ◽  
Fusarium Graminearum ◽  
Fungal Pathogen ◽  
Detailed Knowledge ◽  
Label Free ◽  
Maize Line ◽  
Defense Proteins ◽  
Label Free Quantification ◽  
Host Pathogen ◽  
Free Quantification

AbstractGibberella ear rot is a disease of maize associated with low yields and the production of harmful mycotoxins therein. The disease is caused by the infection of host Zea mays with fungal pathogen Fusarium graminearum. Resistant (CO441) and susceptible (CL30) inbred maize line kernels were inoculated with conidial suspensions of F. graminearum or water (controls). Ears of maize (cobs) from each line were harvested upon maturation and proteins were extracted from the embryo tissue of the kernels to study tissue-specific response of the host. Embryo proteins from both CO441 and CL30 lines were sequenced using mass spectrometry (LC-MS/MS) and quantified using Label Free Quantification (LFQ). Following filtering, 509 proteins were identified. These proteins were grouped into nine functional categories: Fusarium-derived, late embryogenesis abundant, oil-body, metabolism, stress, cellular, protein storage, metabolism, and defense. Defense proteins were up-regulated in response to infection in both CO441 and CL30 lines. Furthermore, F. graminearum derived proteins were only found in CL30 infected kernels suggesting that resistance may be attributed in part to the inability of Fusarium to establish itself in the embryo. To our knowledge this is the first successful application of LFQ mass spectrometry to the study of host-pathogen response to F. graminearum.Biological significanceFungal pathogen Fusarium graminearum is responsible for billion dollar losses in crops and contamination of global grains with harmful mycotoxins. By studying host-pathogen interactions of Fusarium and maize on a proteomic level with resistant and susceptible genotypes, the biological interactions occurring during infection of the maturing seed were characterized. Mature kernels of the F. graminearum susceptible maize line CL30 and resistant CO441 line were dissected to permit a proteomic survey of the new sporophytic generation, the embryo. Detailed knowledge of this Host-pathogen interactome will assist development of new cereal lines resistant to the rot diseases caused by Fusarium graminearum.HighlightsSusceptible (CL30) and Resistant (CO441) lines were injected with water mock or F. graminearum LC-MS/MS of maize embryo protein extracts followed by Label Free Quantification (LFQ) permitted identification, quantification and comparison of proteomes between maize genotypes and treatments Fusarium-derived proteins were abundant only in the susceptible infected embryo Defense proteomes were up-regulated in both lines following infection nsLTP and Protease Inhibitor were significantly over-expressed in the Susceptible line after infection; chitinase and WIP1 were significantly over-expressed in the Resistant line after infection

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

Proteomes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Subina Mehta ◽  
Caleb W. Easterly ◽  
Ray Sajulga ◽  
Robert J. Millikin ◽  
Andrea Argentini ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Software Tools ◽  
Protein Quantification ◽  
Label Free ◽  
Post Translational Modifications ◽  
Label Free Quantification ◽  
File Formats ◽  
Rigorous Testing ◽  
The Galaxy ◽  
Free Quantification

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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