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

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 datase

SUBMITTER: Wen B 

PROVIDER: S-EPMC12592563 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature