Decoding the impact of isolation window selection and QuantUMS filtering in DIA-NN for DIA quantification of peptides and proteins.

Proteomic studies using Data Independent Acquisition (DIA) have gained momentum in all fields of biology. Search engines are evolving to keep up with the latest developments in instrument technology. DIA-NN is the most popular software for DIA analysis under academic use license. The QuantUMS algorithm in DIA-NN improves quantification quality control by calculating three scores (Protein Group MaxLFQ Quality, Empirical Quality, and Quantity Quality) that assess the agreement between MS1 and MS2 features. Here, we show that applying specific cutoffs to these scores can significantly impact results. To enable you to make a more informed decision about what represents a reasonable tradeoff (identification and quantification), we evaluated the impact of different combinations of the scores on data acquired using different isolation windows using a mixture of two-species with known ratio. We compared six different versions of DIA-NN and found high reproducibility across versions, except for version 1.9. We show that filtering by QuantUMS scores removes proteins with low abundance and high coefficients of variation. Finally, we develop the QC4DIANN Shiny application in R language for interactive quality control automation.