Data processing was performed as described in the original manuscript, Navarro et al., 2016. The raw files were converted to mzXML files with centroiding. The resulting mzXML files were processed by the signal extraction (SE) module of DIA-Umpire to generate pseudo-MSMS spectra. The generated pseudo-MS/MS spectra were searched using X!Tandem, Comet and MSGF+ search engines. The output files from the search engines were further analyzed by PeptideProphet and combined by iProphet. FDR filtering was done with PeptideProphet and ProteinProphet. DIA-Umpire's Quant module was for the quantification analysis. The outputs for all-level quantification (FragSummary, PeptideSummary, ProtSummary) were used in further analysis.The quantification result is the same as in RMSV000000250.3. The differential abundance analysis was performed by MSstats (v3.16.2) R package: Top 5 features were used for this reanalysis. Details for data processing and statistical analysis are available in description.pdf ('Methods' folder).
**Publication : Tsai TH, Choi M, Banfai B, Liu Y, MacLean BX, Dunkley T, Vitek O. (2020). Selection of features with consistent proles improves relative protein quantification in mass spectrometry experiments. Molecular & Cellular Proteomics. 2020 March 31, 19(6) 944-959, doi:10.1074/mcp.RA119.001792. PMID: 32234965.
[doi:10.25345/C5KH9C]
[See
results attachment job
for details]
Keywords: MassIVE.quant reviewed - Platinum
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Browse Quantification Results | Browse Metadata |
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