A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis

Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with high data completeness; however, the development of targeted PRM assays based on subsequent bioinformatic predictions can be tedious and time-consuming because of the complexity of the output. We address this limitation with a Python script that rapidly generates a PRM method for the TIMS-QTOF platform using DIA data and a user-defined target list.