MassIVE MSV000088253

Partial Public

Mining the wheat grain proteome.

Description

Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and to filter out the worst performing ones. In this study, we optimised a robust LCMS1 shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios, 2 extraction buffers, 3 sets of proteases, and multiple LC settings. Protein identifications by LCMS2 was used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalized using sample weights, an internal standard, and Quality Controls. Data mining tools found particularly useful to explore the flour proteome are presented. DOI 10.3390/ijms23020713 [doi:10.25345/C5585Q] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Triticum aestivum ; shotgun proteomics ; LC-MS/MS, protease, normalisation, data mining

Contact

Principal Investigators:
(in alphabetical order)
Delphine Vincent, Agriculture Victoria Research, Australia
Submitting User: delphine_1_2
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Experimental Design
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Identification Results
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Quantification Results
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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.