MSPLIT-DIA dataset
Ontology highlight
ABSTRACT:
INSTRUMENT(S): TripleTOF 5600
ORGANISM(S): Homo Sapiens (ncbitaxon:9606) E. Coli
SUBMITTER: Nuno Bandeira
PROVIDER: MSV000079200 | MassIVE | Mon Jul 27 07:29:00 BST 2015
SECONDARY ACCESSION(S): PXD011026
REPOSITORIES: MassIVE
Similar Datasets
Project description:This project stores the search results of MaxQuant2.0.3.0 of 162 HCC cell line raw files and 48 raw files for spectral library generation, and search results of DIA-NN version 1.8 and Spectronaut version15.2 of HepG2, HCCLM3 and TGFB1-stimulated HCCLM3.
2022-11-11 | PXD037159 | Pride
Project description:Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows through a ground truth sample mimicking real differential abundance samples, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library free mode. In spectral library mode we used the independent spectral library prediction tools Prosit and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library free mode or using in silico predicted libraries shows the highest sensitivity maintaining a high reproducibility and accuracy.
2024-05-06 | PXD044981 | Pride
Project description:Data-independent acquisition mass spectrometry (DIA-MS) is a powerful tool for quantitative proteomics, but a well-constructed reference spectral library is crucial to optimize DIA analysis, particularly for low-abundance proteins. In this study, we evaluate the efficacy of a recombinant protein spectral library (rPSL), generated from tryptic digestion of 42 human recombinant proteins, in enhancing the detection and quantification of lower-abundance cancer-associated proteins. Additionally, we generated a combined sample-specific biological-rPSL by integrating the rPSL with a spectral library derived from pooled biological samples. We compared the performance of these libraries for DIA data extraction with standard methods, including sample-specific biological spectral library and library-free DIA methods. Our specific focus was on quantifying cancer-associated proteins, including key enzymes involved in kynurenine pathway, across patient-derived tissues and cell lines. Both rPSL and biological-rPSL DIA approaches provided significantly improved coverage of lower- abundance proteins, enhancing sensitivity and more consistent protein quantification across matched tumour and adjacent noncancerous tissues from breast and colorectal cancer patients and in cancer cell lines. Overall, our study demonstrates that rPSL and biological-rPSL coupled with DIA-MS workflows, can address the limitations of both biological library-based and library-free DIA methods, offering a robust approach for quantifying low-abundance cancer-associated proteins in complex biological samples.
2025-05-15 | PXD061696 | Pride
Project description:Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method choice to achieve sensitive, accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and crustaceans a choice model for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Students t-test, P value<0.05) in the number of identifications achieved from TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications were found in the library-search compared to the library-free search. This work highlights the necessity for use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.
2023-05-04 | MSV000091860 | MassIVE
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.
2022-01-17 | PXD028901 | Pride
Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.
2019-11-08 | PXD012988 | Pride
Project description:The zebrafish (Danio rerio) retina has been increasingly used for studying vertebrate visual processing and ocular growth regulation as it has a high anatomical and physiological similarity to the human eye. Recently, the integration of trapped ion mobility spectrometry (TIMS) with parallel accumulation–serial fragmentation (PASEF), has emerged as a powerful approach for proteomics analysis. In this study, a retina specific hybrid spectral library was constructed for quantitative proteomics analysis of the zebrafish retina. We integrated data dependent acquisition PASEF (DDA PASEF) and data independent acquisition PASEF (DIA PASEF) datasets acquired from 16 individual retinal protein lysates on a Bruker timsTOF Pro 2 mass spectrometer to generate the hybrid spectral library. The Pulsar search engine in Spectronaut 22 was used for hybrid library generation. The resulting hybrid library comprised 8,551 precursors, 7,866 unique peptides, and 1,494 protein groups (1% FDR), representing 44.5% of the reviewed zebrafish reference proteome.16 DIA PASEF runs from individual samples were then analyzed in both library based (using the hybrid library) and library free settings in Spectronaut (version 22) and DIA NN (version 2.3.2). It demonstrated that the hybrid library outperformed library free approaches, with improved peptide and precursor identifications across both proteomics platforms.
2026-06-17 | PXD076440 | Pride
Project description:The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that this robust quantification and in-depth retinal-specific spectral library will contribute to a better understanding of the proteome complexity of retina. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.
2024-07-19 | PXD046983 | Pride
Project description:Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined. We found, that while libraries of prefractionationed samples are generally larger, the actually yielded identifications are not increased compared to repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantifications is also highly dependent on the applied spectra library and also whether the peptide or protein level is analysed. Overall, the reproducibility and accuracy of DIA is superior to DDA in all analysed approaches.
2019-11-08 | PXD014956 | Pride