Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis
Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15456-w |
| Summary: | Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction. |
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| ISSN: | 2041-1723 |