Decoding the Impact of Isolation Window Selection and QuantUMS Filtering in DIA-NN for DIA Quantification of Peptides and Proteins.

Abstract

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 an 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 the results. To enable you to make a more informed decision about what represents a reasonable trade-off (identification and quantification), we evaluated the impact of different combinations of the scores on data acquired using different isolation windows and a mixture of two species with a known ratio. To test consistency and reproducibility across the six different versions of DIA-NN, we compared them and found high reproducibility except for version 1.9. We show that filtering by QuantUMS scores removes proteins with low abundances and high coefficients of variation. Finally, we developed the QC4DIANN Shiny application in the R language for interactive quality control automation.