FragPipe is recognized as one of the fastest computational platforms in proteomics, making it a practical solution for the rapid quality control of high-throughput sample analyses. Starting with version 23.0, FragPipe introduced the “Generate Summary Report” feature, offering .pdf reports with essential quality control metrics to address the challenge of intuitively assessing large-scale proteomics data. While traditional spreadsheet formats (e.g., tsv files) are accessible, the complexity of the data often limits user-friendly interpretation. To further enhance accessibility, PSManalyst, a Shiny-based R application, was developed to process FragPipe output files (psm.tsv, protein.tsv, and combined_protein.tsv) and provide interactive, code-free data visualization. Users can filter peptide-spectrum matches (PSMs) by quality scores, visualize protease cleavage fingerprints as heatmaps and SeqLogos, and access a range of quality control metrics and representations such as peptide length distributions, ion densities, mass errors, and wordclouds for overrepresented peptides. The tool facilitates seamless switching between PSM and protein data visualization, offering insights into protein abundance discrepancies, samplewise similarity metrics, protein coverage, and contaminants evaluation. PSManalyst leverages several R libraries (lsa, vegan, ggfortify, ggseqlogo, wordcloud2, tidyverse, ggpointdensity, and plotly) and runs on Windows, MacOS, and Linux, requiring only a local R setup and an IDE. The app is available at (https://github.com/41ison/PSManalyst.

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.