Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

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 in silico 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 in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico 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. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool.

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Publication Details

Journal
Nature Communications
Published
2025-11-06
DOI
https://doi.org/10.1038/s41467-025-64928-4
Citations
5
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
3.78

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article

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Wen‐Feng Zeng, Judit Villén, Bo Wen, David Shteynberg et al.
5 citations
Nature Communications
Advanced Proteomics Techniques and Applications
3.78
article

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Wen‐Feng Zeng, Judit Villén, Bo Wen, David Shteynberg, Brendan MacLean, Michael J. MacCoss, Alexis Chang, William Stafford Noble, Michael Riffle, Chris Hsu, Miranda C. Mudge, Brook L. Nunn, Matthew D. Berg
article en
5 citations

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 in silico 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 in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico 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. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool.

Nature CommunicationsVol. 16(1)
University of Washington (US), Max Planck Institute of Biochemistry (DE)
National Science Foundation, National Institutes of Health, Intelligence Advanced Research Projects Activity, National Institute of General Medical Sciences, Army Research Office
Openalex Percentile: Top 6%
Advanced Proteomics Techniques and Applications
3.78
DOIOpen Access PDFOpenAlex
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