ArticleNature communications2025
Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- Physiological and Proteomic Insights into Melatonin-Mediated Regulation of Copper Toxicity in the CrayfishInternational journal of molecular sciences · 2026Article
- Revisiting resonance-excitation collision-induced dissociation for data-independent acquisition.bioRxiv : the preprint server for biology · 2026Article
- Prioritizing peptides for targeted mass spectrometry experiments using deep learning.bioRxiv : the preprint server for biology · 2026Article
- A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.bioRxiv : the preprint server for biology · 2026Article
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Multimodal analysis of molecular remodeling in aging spleen identified global and cell type specific changes.bioRxiv : the preprint server for biology · 2026Article
- Carafe2 enables high qualitybioRxiv : the preprint server for biology · 2026Article
- Serum proteomics reveals distinct phenotypic signatures to IL-6 blockade between two immunotherapies.bioRxiv : the preprint server for biology · 2026Article
- Proteomic profiling of human omental and subcutaneous adipose tissue in individuals with a broad range of BMI.Scientific data · 2026Article
- Is Protein Quantification and Physical Normalization Always Necessary in Proteomics?bioRxiv : the preprint server for biology · 2026Article
- Proteomic Profiling of Human Omental and Subcutaneous Adipose Tissue in Individuals with a Broad Range of BMI.bioRxiv : the preprint server for biology · 2026Article
- Capturing protein-protein interactions in plants: recent advances, challenges, and opportunities.Frontiers in molecular biosciences · 2026Review
- Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes.Journal of proteome research · 2025Article
- Evaluation of a Prototype Orbitrap Astral Zoom Mass Spectrometer for Quantitative Proteomics─Beyond Identification Lists.Journal of proteome research · 2025Article
- Evaluation of a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics - Beyond identification lists.bioRxiv : the preprint server for biology · 2025Article
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13 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.