AI Article Synopsis
- Data-independent acquisition (DIA) mass spectrometry is gaining popularity in quantitative proteomics due to its effectiveness in data analysis.
- Creating reliable spectral libraries for DIA is challenging, as most current libraries come from data-dependent acquisition (DDA) data or predictions based on DDA.
- The study introduces Carafe, a tool that generates specific spectral libraries by using deep learning directly on DIA data, showing better performance in predicting ion intensity and detecting peptides compared to existing DDA models.
Article Abstract
Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11507862 | PMC |
| http://dx.doi.org/10.1101/2024.10.15.618504 | DOI Listing |
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