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◆ Journal of Proteome Research2025-12-30· Interpretability

Improvements to Casanovo, a Deep Learning <i>De Novo</i> Peptide Sequencer

Gwenneth Straub, Varun Ananth, William E. Fondrie, Chris Hsu, Daniela Klaproth-Andrade, Marina Pominova, Michael Riffle, Justin J. Sanders, Bo Wen, Lingwen Xu, Melih Yilmaz, Michael J. MacCoss, Sewoong Oh, Wout Bittremieux, William Stafford Noble

原始摘要(英文原文)· Original abstract
peptide sequencing from mass spectrometry and proteomics data. Here, we report on a series of enhancements to Casanovo, aimed at improving the interpretability of the scores assigned to predicted peptides, generalizing the software for use in database searches, speeding up training and prediction runtimes, and providing workflows and visualization tools to facilitate adoption of Casanovo and interpretation of its results. Our goal is to make Casanovo accurate and easy to use for applications such as metaproteomics, antibody sequencing, immunopeptidomics, and the discovery of novel peptide sequences in standard proteomics analyses. Casanovo is available as open source at https://github.com/Noble-Lab/casanovo.
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Improvements to Casanovo, a Deep Learning <i>De Novo</i> Peptide Sequencer — 科研速览 Science Skim