Targeted proteomics data interpretation with DeepMRM

Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool. [Display omitted] •DeepMRM utilizes AI for object detection in targeted proteomics data interpretation•DeepMRM outperforms Skyline in quantification accuracy•DeepMRM shows robust performance across MRM, PRM, and DIA data•DeepMRM is available as a Windows desktop application and a Skyline external tool In clinical proteomics, targeted proteomics approaches like multiple-reaction monitoring (MRM) or parallel-reaction monitoring (PRM) are widely used, but their application is often hampered by labor-intensive and error-prone manual data interpretation. Existing computational methods, while helpful, often demand a substantial degree of manual intervention or decoy-transition approaches, constraining the throughput and efficiency of clinical proteomics assays. To address these challenges, we developed DeepMRM, a targeted proteomics data interpretation package that facilitates high-throughput analysis and enhances the reproducibility and scalability of targeted proteomics in clinical settings. Park et al. present DeepMRM, a software package leveraging deep learning for object detection to minimize manual intervention in targeted proteomics data analysis. DeepMRM promotes high-throughput analysis and enhances the reproducibility and scalability of targeted proteomics in clinical settings.