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UPS1-yeast standard analysis - Technical Brief Calibration Plot for Proteomics (CP4P): A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments

ABSTRACT: Label-free quantification of various concentrations of Universal Proteomic Standard (UPS1, Sigma-Aldrich) spiked in yeast extract.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Yohann Couté

LAB HEAD: Christophe Bruley

PROVIDER: PXD002370 | Pride | 2015-11-27

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	C-25fmol-R1_QEx2_000090.raw	Raw
	C-25fmol-R2_QEx2_000099.raw	Raw
	C-25fmol-R3_QEx2_000116.raw	Raw
	D-10fmol-R1_QEx2_000089.raw	Raw
	D-10fmol-R2_QEx2_000098.raw	Raw

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Publications

Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments.

Giai Gianetto Quentin Q Combes Florence F Ramus Claire C Bruley Christophe C Couté Yohann Y Burger Thomas T

Proteomics 20160101 1

In MS-based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p-values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p-values are well-calibrated; the false discovery control being spuriously ...[more]

PMID: 26572953

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yeast-UPS1 standard LC-MS/MS dataset

Project description:Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years due to the technical improvement of mass spectrometers, and now allow to extensively characterize complex protein mixtures. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, which appear as an attractive way to analyze differential protein expression in complex biological samples. Classical label-free quantitative workflows are based either on spectral counting of MS/MS sequencing scans for each protein, or on the extraction of peptide ion peak area values in the LC-MS map composed of all the survey MS scans acquired during the chromatographic gradient. However, the computational processing of the data for label-free quantification still remains a challenge. Here, we provide a dual proteomic standard composed of an equimolar mixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate, that was used to benchmark several label-free quantitative workflows, involving different software packages developed in recent years. This experimental design allowed to finely assess their performances in terms of sensitivity and false discovery rate, by measuring the number of true and false-positive (respectively UPS1 or yeast background proteins found as differential). This dataset can also be used to benchmark other label-free workflows, adjust software parameter settings, improve algorithms for extraction of the quantitative metrics from raw MS data, or evaluate downstream statistical methods

2015-12-01 | PXD001819 | Pride

S. cerevisiae DDA orbitrap-based benchmark datasets. C4PR_LIV

Project description:In this study, label free peptide intensities from a well quantitatively characterised yeast cell lysate were acquired on two orbitrap mass spectrometers (LTQ-Velos and Q Exactive HF). Additionally, samples containing Universal Proteomics Standard and the Proteomics Dynamic range Standard (http://www.sigmaaldrich.com/life-science/proteomics/mass-spectrometry/ups1-and-ups2-proteomic.html) in a yeast background were also acquired. The absolute abundances of over 340 proteins present in that yeast lysate (determined in a parallel SRM-based study) were then used in order to determine the flyability (or detectability) of thousands of peptides. The flyability scores, termed the ‘F-factors’, reflect how well a peptide ionises in a complex chromatographically separated sample. Specifically, F-factors are calculated by normalising peptide precursor ion intensity by the absolute abundance of its parent protein. Based on the analysis of the six datasets deposited here (reflecting different gradients and instrument platforms) the study found that physicochemical properties including peptide length, charge and hydrophobicity are predictors of peptide detectability. Furthermore, it was established that hydrophobicity has a non-linear relationship with detectability and that coelution with competing ions significantly affects peptide detectability. The analysis based on the data deposited here suggests that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex chromatographically-separated peptide mixtures. The concept of F-factors will also undoubtedly assist in better surrogate selection in targeted mass spectrometry studies and allow more accurate calibration of peptide ion signal in label-free workflows.

2017-08-09 | PXD003472 | Pride

Benchmarking DIA data analysis workflows

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