Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens Saccharomyces Cerevisiae
SUBMITTER: 
Bo Wen
PROVIDER: PXD075483 | panorama | Tue Mar 31 00:00:00 BST 2026
REPOSITORIES: PanoramaPublic
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Project description: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 developed 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.
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Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
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Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
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Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.
2018-03-26 | MTBLS417 | MetaboLights
Project description:Data-independent mass spectrometry is the method of choice for deep, consistent and accurate single-shot profiling in bottom-up proteomics. While classic workflows required auxiliary DDA-MS analysis of subject samples to derive prior knowledge spectral libraries for targeted quantification from DIA-MS maps, library-free approaches based on in silico predicted libraries promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses, however, is limited, in part, by large library size and persistent deviations from experimental data. We present MSLibrarian, a workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library approach. Predicted-vs-observed comparisons enable optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups and optimization of library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded species mixture experiment and quantitative ratio-validation confirms gains of up to 9 % on precursor and 7 % protein level at equivalent FDR control and validation criteria. MSLibrarian has been implemented as open-source R software package and, with step-by-step usage instructions, is availabe at https://github.com/MarcIsak/MSLibrarian.
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Project description:We have developed DIA-NN, an integrated software suite for fully-automated processing of raw SWATH/DIA data. DIA-NN enables deep and confident proteome coverage with fast chromatographic methods, paving the way for a new generation of high-throughput proteomics. This data set illustrates the capabilities of DIA-NN when analysing microflow SWATH data acquired with 19 - 23 minute gradients as well as the use of DIA-NN to generate spectral libraries directly from SWATH/DIA data.
2019-11-06 | PXD014690 | Pride
Project description:<p>Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets.</p><p><br></p><p>In MTBLS1333, 58 lipid mediator standards were used to study the lipid fragmentation, build optimal collision energy models, create in-silico spectral libraries and spike-in as internal standards. They were purchased from Cayman Chemical (Ann Arbor, Michigan, USA). Lipid fragment masses of mediators were validated with the Metlin database (https://metlin.scripps.edu/).</p><p><br></p><p>Studies linked to this manuscript include;</p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1333' rel='noopener noreferrer' target='_blank'>MTBLS1333; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Thermo QExactive HF</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1334' rel='noopener noreferrer' target='_blank'>MTBLS1334; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Agilent QTof</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1369' rel='noopener noreferrer' target='_blank'>MTBLS1369; LipidCreator: Platelet isolation and stimulation - Targeted Phospho- and Glycerolipid Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1375' rel='noopener noreferrer' target='_blank'>MTBLS1375; LipidCreator: Targeted lipidomics analysis of Human Plasma samples and comparison with NIST SRM 1950 standard</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1376' rel='noopener noreferrer' target='_blank'>MTBLS1376; LipidCreator: Targeted lipidomics analysis of S. cerevisiae to determine true and false identification</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1381' rel='noopener noreferrer' target='_blank'>MTBLS1381; LipidCreator: Platelet isolation and stimulation - Targeted Lipid Mediator Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1382' rel='noopener noreferrer' target='_blank'>MTBLS1382; LipidCreator: Platelet isolation and stimulation - DIA Lipid Mediator Validation</a></p>
2020-04-15 | MTBLS1333 | MetaboLights
Project description:<p>Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets.</p><p><br></p><p>In MTBLS1382, to validate lipid mediator species identified with the Qtrap instrument (<a href='https://www.ebi.ac.uk/metabolights/MTBLS1381' rel='noopener noreferrer' target='_blank'>https://www.ebi.ac.uk/metabolights/MTBLS1381</a>), the Thermo QEx HF was used to perform high resolution MS full scan (HR-FS) and data independent acquisition (DIA) analyses between 200-400 m/z with a 20 m/z step size. DIA method preparation and data analysis were performed with Skyline. We used LipidCreator as an external tool in Skyline to generate a collision-energy specific transition list for the mediators 12-HETE, 13-HODE, 15-HETE, arachidonic acid (AA),<strong> </strong>docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and thromboxane B2 (TXB2) and their deuterated counterparts 12-HETE-d8, 13-HODE-d4, 15-HETE-d8, AA-d8, DHA-d5, EPA-d5 and TXB2-d4, at a normalized collision energy NCE=21. We also used LipidCreator's relative fragment intensity prediction model to generate a custom spectral library for the light and heavy mediators. We transfered both the transition list and the spectral library directly from LipidCreator to Skyline and exported the dot product values after manual fragment peaks review.</p><p><br></p><p>Studies linked to this manuscript include;</p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1333' rel='noopener noreferrer' target='_blank'>MTBLS1333; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Thermo QExactive HF</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1334' rel='noopener noreferrer' target='_blank'>MTBLS1334; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Agilent QTof</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1369' rel='noopener noreferrer' target='_blank'>MTBLS1369; LipidCreator: Platelet isolation and stimulation - Targeted Phospho- and Glycerolipid Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1375' rel='noopener noreferrer' target='_blank'>MTBLS1375; LipidCreator: Targeted lipidomics analysis of Human Plasma samples and comparison with NIST SRM 1950 standard</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1376' rel='noopener noreferrer' target='_blank'>MTBLS1376; LipidCreator: Targeted lipidomics analysis of S. cerevisiae to determine true and false identification</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1381' rel='noopener noreferrer' target='_blank'>MTBLS1381; LipidCreator: Platelet isolation and stimulation - Targeted Lipid Mediator Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1382' rel='noopener noreferrer' target='_blank'>MTBLS1382; LipidCreator: Platelet isolation and stimulation - DIA Lipid Mediator Validation</a></p>
2020-04-15 | MTBLS1382 | MetaboLights
Project description:Data-independent acquisition (DIA) proteomics allows systematic and unbiased measurement of protein samples and enables fast quantitative analysis of large cohorts of samples. However, sample-specific spectral libraries are usually required prior to perform DIA experiments. The libraries are built by data-dependent acquisition (DDA) proteomic analysis on the same samples normally with pre-fractionation, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning based approach to generate in silico spectral libraries for DIA analysis. We benchmarked DeepDIA with HeLa and mixed proteome data sets, showing that the quality of in silico spectral libraries is comparable to that of experimental spectral libraries. We further demonstrated that DeepDIA can be performed on human cell lines and human serum without pre-knowledge of peptides lists by DDA experiments. Compared to the state-of-the-art protocol using DDA-based spectral library with high abundance protein depletion and pre-fractionation, the number of identified and quantified proteins from human serum was increased by >100% using DeepDIA. Accuracy of the identification results was validated using a standard mixture containing >800 stable isotope labelled reference peptides from >500 proteins in human plasma. We expect this work contributing to the studies of quantitative proteomics and especially blood proteomics, whereas expanding the toolbox for DIA proteomics.
2019-12-02 | PXD014108 |
Project description:<p>Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets.</p><p><br></p><p>In MTBLS1334, 55 lipid mediator standards were used to study the lipid fragmentation on the Agilent QTof 6545 MS platform (Agilent Technologies, Waldbronn, Germany), build optimal collision energy models and create an in-silico spectral library for use with LipidCreator. Lipid fragment masses of mediators were validated with the Metlin database (https://metlin.scripps.edu/).</p><p><br></p><p>Studies linked to this manuscript include;</p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1333' rel='noopener noreferrer' target='_blank'>MTBLS1333; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Thermo QExactive HF</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1334' rel='noopener noreferrer' target='_blank'>MTBLS1334; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Agilent QTof</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1369' rel='noopener noreferrer' target='_blank'>MTBLS1369; LipidCreator: Platelet isolation and stimulation - Targeted Phospho- and Glycerolipid Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1375' rel='noopener noreferrer' target='_blank'>MTBLS1375; LipidCreator: Targeted lipidomics analysis of Human Plasma samples and comparison with NIST SRM 1950 standard</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1376' rel='noopener noreferrer' target='_blank'>MTBLS1376; LipidCreator: Targeted lipidomics analysis of S. cerevisiae to determine true and false identification</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1381' rel='noopener noreferrer' target='_blank'>MTBLS1381; LipidCreator: Platelet isolation and stimulation - Targeted Lipid Mediator Profiling</a></p><p><a href='https://www.ebi.ac.uk/metabolights/MTBLS1382' rel='noopener noreferrer' target='_blank'>MTBLS1382; LipidCreator: Platelet isolation and stimulation - DIA Lipid Mediator Validation</a></p>
2020-04-15 | MTBLS1334 | MetaboLights