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- W3015750191 abstract "Abstract Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. 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." @default.
- W3015750191 created "2020-04-17" @default.
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- W3015750191 date "2020-04-09" @default.
- W3015750191 modified "2023-10-12" @default.
- W3015750191 title "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis" @default.
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- W3015750191 doi "https://doi.org/10.1038/s41467-020-15456-w" @default.
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