MsTargetPeaker: A Quality-Aware Deep Reinforcement Learning Approach for Peak Identification in Targeted Proteomics

Targeted mass spectrometry enables precise peptide quantification by identifying high-quality chromatographic peaks for area integration. Automated peak identification remains challenging, particularly for low-abundance targets, because of interference and noise. Existing approaches typically rely on two supervised learning models, one for selecting peak regions and the other for performing downstream quality control in a separate postprocessing step. However, deferring quality assessment to a separate stage may limit the ability to refine peak boundaries in pursuit of improved quality, as the initial selection is performed without explicit awareness of quality-related criteria. In this study, we present MsTargetPeaker, a quality-aware search procedure for identifying peak regions in targeted proteomics data. The method employs a reinforcement learning agent to guide Monte Carlo tree search to efficiently explore chromatograms and localize target peaks while minimizing interference. Peak quality is dynamically assessed during the search via a custom-designed reward function, which prioritizes regions with desirable peak characteristics and enables accurate and robust boundary determination. The reward function further incorporates cross-sample consensus profiles of candidate boundaries to improve the identification of low-quality or ambiguous signals. These innovations support fine-grained peak identification, enhancing both peak quality and quantification precision. In addition, the transparent reward calculation allows MsTargetPeaker to generate interpretable diagnostic quality reports, providing comprehensive metrics across transitions, peak groups, and sample replicates. This facilitates efficient detection of problematic cases for manual curation. Collectively, MsTargetPeaker offers a practical advancement toward robust and automated peak identification in targeted proteomics.