Bo’s paper Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis has been published in Nature Communications. Congratulations, Bo! Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. In this paper, we propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm AutoRT for accurate retention time prediction. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.
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