Fig. 5: Intensity prediction improves search quality of ECD, EID and UVPD DIA data. | Nature Methods

Fig. 5: Intensity prediction improves search quality of ECD, EID and UVPD DIA data.

From: Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage

Fig. 5: Intensity prediction improves search quality of ECD, EID and UVPD DIA data.The alternative text for this image may have been generated using AI.

Number of PSMs, peptides and proteins identified at 1% FDR in the UVPD, EID and ECD DIA data of unfractionated tryptic digests of human, A. thaliana and E. coli proteins. The analysis was performed in the FragPipe platform using the MSFragger search engine with Prosit predictions of fragment ion intensities implemented within the MSBooster module. The numbers of shared, gained and lost identifications correspond to the analysis with MSBooster ’on’ as compared with the results obtained with MSBooster ’off’.

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