STAVER: A Standardized Benchmark Dataset-Based Algorithm for Effective Variation Reduction in Large-Scale DIA-MS Data

This project focuses on developing and applying STAVER, an innovative DIA algorithm designed to eliminate non-biological noise and variability from the large-scale DIA-MS study dataset analyses. STAVER is a flexible framework that utilizes prior knowledge regarding peptide separation coordinates (RT) and fragment ion intensities from the standard benchmark datasets, which effectively mitigates non-biological noise potential during library searches, enhancing spectrum identifications and protein quantification accuracy. Furthermore, the robustness and broad applicability of STAVER were validated in multiple large-scale DIA datasets from different platforms and laboratories, demonstrating significantly improved precision and reproducibility of protein quantification. It facilitates the comparative and integrative analysis of DIA datasets across different platforms and laboratories, enhancing the consistency and reliability of findings in clinical research. The project aims to promote the adoption of hybrid library search and improve the sensitivity and quality of DIA proteomics data through the open-source STAVER software package.