Data-independent acquisition (DIA) mass spectrometry is a technique used in proteomics to identify and quantify proteins in complex biological samples. While this comprehensive approach yields more complete and reproducible protein profiles than data-independent acquisition (DDA), the resulting data are substantially larger and more complex, presenting significant challenges for data analysis and interpretation. These challenges can be effectively addressed using dedicated workflow managers that support parallel execution of complex analysis pipelines on high-performance computing infrastructure. Nextflow, in particular, is well-suited for streamlining data analysis, as it automates key aspects of workflow management, allowing researchers to efficiently analyze large-scale data sets with minimal manual intervention. Here, we describe glaDIAtor-nf, a Nextflow version of our software package glaDIAtor for untargeted analysis of DIA mass spectrometry proteomics data. We first demonstrate its technical accuracy through rigorous testing on gold standard data sets. Building on this, we then reveal known proteome patterns from public breast cancer data that remained hidden in the processed data of the original study. This illustrates the potential of reanalyzing the accumulating public data, but also highlights the need for convenient tools to facilitate such reanalysis in large-scale.