Fig. 2: Optimizing data acquisition with deconvolution in mind. | Nature Methods

Fig. 2: Optimizing data acquisition with deconvolution in mind.

From: Unifying the analysis of bottom-up proteomics data with CHIMERYS

Fig. 2

a, PSMs identified at 1% run-specific PSM-level FDR based on Sequest HT (orange), MSFragger (green) and CHIMERYS (blue) from 1-h HeLa single-shot measurements (n = 1), acquired using various Orbitrap generations. b, PSM, peptide group and protein group identifications based on Sequest HT (orange), MSFragger (green), CHIMERYS (blue) and CHIMERYS after removal of low-abundance peaks (light blue) from a 1-h HeLa single-shot measurement (n = 1), acquired using collision-induced dissociation (CID) fragmentation with ion trap readout. FDR was controlled at 1% at the run-specific PSM, peptide group (only available for Sequest HT and CHIMERYS) and protein group level, respectively. c, PSM, peptide group and protein group identifications based on Sequest HT (orange), MSFragger (green) and CHIMERYS (blue) from pancreatic mouse cell single-shot measurements using various gradient lengths (n = 1). FDR was controlled at 1% at the run-specific PSM, peptide group (only available for Sequest HT and CHIMERYS) and protein group level, respectively. d, Distribution of the number of PSMs per MS2 spectrum from 1-h pancreatic mouse cell single-shot measurements, acquired using different isolation window widths (n = 1). FDR was controlled at 1% at the run-specific PSM level. e, PSM, peptide group and protein group identifications based on Sequest HT (orange), MSFragger (green) and CHIMERYS (blue) from 1-h pancreatic mouse cell single-shot measurements, acquired using different isolation window widths (n = 1). FDR was controlled at 1% at the run-specific PSM, peptide group (only available for Sequest HT and CHIMERYS) and protein group level, respectively.

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