Benchmarking label-free quantification based on data-independent acquisition (DIA) workflows

Label-free quantification based on data-independent acquisition (DIA) workflows is increasingly popular. Several software tools have been recently published or are commercially available. The present study focuses on the critical evaluation of three different software packages (Progenesis, synapter and ISOQuant) supporting ion-mobility enhanced DIA data. In order to benchmark the label-free quantification performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, E.coli). This model data set simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and down-regulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on the variation of reported protein quantities between technical replicates.