Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics
Bo Wen, Chris Hsu, David Shteynberg, Wen‐Feng Zeng, Michael Riffle, Alexis Chang, Miranda C. Mudge, Brook L. Nunn, Brendan MacLean, Matthew D. Berg, Judit Villén, Michael J. MacCoss, William Stafford Noble
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.