scholarly journals PIQMIe: a web server for semi-quantitative proteomics data management and analysis

2014 ◽  
Vol 42 (W1) ◽  
pp. W100-W106 ◽  
Author(s):  
Arnold Kuzniar ◽  
Roland Kanaar
Keyword(s):  
Data Management ◽  
Quantitative Proteomics ◽  
Web Server ◽  
Proteomics Data

scholarly journals Oxonium Ion Guided Analysis of Quantitative Proteomics Data Reveals Site-Specific O-Glycosylation of Anterior Gradient Protein 2 (AGR2)

2021 ◽  
Vol 22 (10) ◽  
pp. 5369
Author(s):  
Martina Pirro ◽  
Yassene Mohammed ◽  
Arnoud H. de Ru ◽  
George M. C. Janssen ◽  
Rayman T. N. Tjokrodirijo ◽  
...  
Keyword(s):  
Cell Lines ◽  
Quantitative Proteomics ◽  
Glycosylation Site ◽  
Total Cell ◽  
Tandem Mass ◽  
Proteomics Data ◽  
Site Specific ◽  
Oxonium Ions ◽  
Tandem Mass Tag ◽  
Colorectal Cancer Cell Lines

Developments in mass spectrometry (MS)-based analyses of glycoproteins have been important to study changes in glycosylation related to disease. Recently, the characteristic pattern of oxonium ions in glycopeptide fragmentation spectra had been used to assign different sets of glycopeptides. In particular, this was helpful to discriminate between O-GalNAc and O-GlcNAc. Here, we thought to investigate how such information can be used to examine quantitative proteomics data. For this purpose, we used tandem mass tag (TMT)-labeled samples from total cell lysates and secreted proteins from three different colorectal cancer cell lines. Following automated glycopeptide assignment (Byonic) and evaluation of the presence and relative intensity of oxonium ions, we observed that, in particular, the ratio of the ions at m/z 144.066 and 138.055, respectively, could be used to discriminate between O-GlcNAcylated and O-GalNAcylated peptides, with concomitant relative quantification between the different cell lines. Among the O-GalNAcylated proteins, we also observed anterior gradient protein 2 (AGR2), a protein which glycosylation site and status was hitherto not well documented. Using a combination of multiple fragmentation methods, we then not only assigned the site of modification, but also showed different glycosylation between intracellular (ER-resident) and secreted AGR2. Overall, our study shows the potential of broad application of the use of the relative intensities of oxonium ions for the confident assignment of glycopeptides, even in complex proteomics datasets.

scholarly journals Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future

2021 ◽  
Vol 20 ◽  
pp. 100071
Author(s):  
Nuno Bandeira ◽  
Eric W. Deutsch ◽  
Oliver Kohlbacher ◽  
Lennart Martens ◽  
Juan Antonio Vizcaíno
Keyword(s):  
Data Management ◽  
Proteomics Data ◽  
The Future ◽  
Current Practices

scholarly journals Quantitative Proteomics Analysis Reveals the Function of the Putative Ester Cyclase UvEC1 in the Pathogenicity of the Rice False Smut Fungus Ustilaginoidea virens

2021 ◽  
Vol 22 (8) ◽  
pp. 4069
Author(s):  
Xiaoyang Chen ◽  
Zhangxin Pei ◽  
Pingping Li ◽  
Xiabing Li ◽  
Yuhang Duan ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Fungal Pathogens ◽  
Protein Localization ◽  
Proteomics Data ◽  
Ustilaginoidea Virens ◽  
Rice False Smut ◽  
Oxidative Stresses ◽  
Tandem Mass Tag ◽  
Cyclase Domain ◽  
False Smut

Rice false smut is a fungal disease distributed worldwide and caused by Ustilaginoidea virens. In this study, we identified a putative ester cyclase (named as UvEC1) as being significantly upregulated during U. virens infection. UvEC1 contained a SnoaL-like polyketide cyclase domain, but the functions of ketone cyclases such as SnoaL in plant fungal pathogens remain unclear. Deletion of UvEC1 caused defects in vegetative growth and conidiation. UvEC1 was also required for response to hyperosmotic and oxidative stresses and for maintenance of cell wall integrity. Importantly, ΔUvEC1 mutants exhibited reduced virulence. We performed a tandem mass tag (TMT)-based quantitative proteomic analysis to identify differentially accumulating proteins (DAPs) between the ΔUvEC1-1 mutant and the wild-type isolate HWD-2. Proteomics data revealed that UvEC1 has a variety of effects on metabolism, protein localization, catalytic activity, binding, toxin biosynthesis and the spliceosome. Taken together, our findings suggest that UvEC1 is critical for the development and virulence of U. virens.

scholarly journals Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user's perspective

2021 ◽  
Author(s):  
Alejandro Fernandez-Vega ◽  
Federica Farabegoli ◽  
Maria Mercedes Alonso-Martinez ◽  
Ignacio Ortea
Keyword(s):  
Data Analysis ◽  
Quantitative Proteomics ◽  
Real Life ◽  
Analysis Tool ◽  
Proteomics Data ◽  
Mass Spectrometers ◽  
Data Independent Acquisition ◽  
Proteomics Experiment ◽  
Under Sampling ◽  
The One

Data-independent acquisition (DIA) methods have gained great popularity in bottom-up quantitative proteomics, as they overcome the irreproducibility and under-sampling limitations of data-dependent acquisition (DDA). diaPASEF, recently developed for the timsTOF Pro mass spectrometers, has brought improvements to DIA, providing additional ion separation (in the ion mobility dimension) and increasing sensitivity. Several studies have benchmarked different workflows for DIA quantitative proteomics, but mostly using instruments from Sciex and Thermo, and therefore, the results are not extrapolable to diaPASEF data. In this work, using a real-life sample set like the one that can be found in any proteomics experiment, we compared the results of analyzing PASEF data with different combinations of library-based and library-free analysis, combining the tools of the FragPipe suite, DIA-NN and including MS1-level LFQ with DDA-PASEF data, and also comparing with the workflows possible in Spectronaut. We verified that library-independent workflows, not so efficient not so long ago, have greatly improved in the recent versions of the software tools, and now perform as well or even better than library-based ones. We report here information so that the user who is going to conduct a relative quantitative proteomics study using a timsTOF Pro mass spectrometer can make an informed decision on how to acquire (diaPASEF for DIA analysis, or DDA-PASEF for MS1-level LFQ) the samples, and what can be expected depending on the data analysis tool used, among the different alternatives offered by the recently optimized tools for TIMS-PASEF data analysis.

scholarly journals obaDIA: one-step biological analysis pipeline for data-independent acquisition and other quantitative proteomics data

2020 ◽  
Author(s):  
Jun Yan ◽  
Hongning Zhai ◽  
Ling Zhu ◽  
Sasha Sa ◽  
Xiaojun Ding
Keyword(s):  
Protein Level ◽  
Quantitative Proteomics ◽  
Differential Expression Analysis ◽  
Biological Significance ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Proteomics Data ◽  
Data Independent Acquisition ◽  
One Step ◽  
Automatic Tool

Abstract Motivation Data mining and data quality evaluation are indispensable constituents of quantitative proteomics, but few integrated tools available. Results We introduced obaDIA, a one-step pipeline to generate visualizable and comprehensive results for quantitative proteomics data. obaDIA supports fragment-level, peptide-level and protein-level abundance matrices from DIA technique, as well as protein-level abundance matrices from other quantitative proteomic techniques. The result contains abundance matrix statistics, differential expression analysis, protein functional annotation and enrichment analysis. Additionally, enrichment strategies which use total proteins or expressed proteins as background are optional, and HTML based interactive visualization for differentially expressed proteins in the KEGG pathway is offered, which helps biological significance mining. In short, obaDIA is an automatic tool for bioinformatics analysis for quantitative proteomics. Availability and implementation obaDIA is freely available from https://github.com/yjthu/obaDIA.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Assembly of the U5 snRNP component PRPF8 is controlled by the HSP90/R2TP chaperones

2017 ◽  
Vol 216 (6) ◽  
pp. 1579-1596 ◽  
Author(s):  
Anna Malinová ◽  
Zuzana Cvačková ◽  
Daniel Matějů ◽  
Zuzana Hořejší ◽  
Claire Abéza ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Mrna Splicing ◽  
Splicing Factor ◽  
Dependent Manner ◽  
Proteomics Data ◽  
Small Nuclear Ribonucleoprotein ◽  
Crucial Component ◽  
Potential Link ◽  
Nuclear Ribonucleoprotein ◽  
New Interactions

Splicing is catalyzed by the spliceosome, a complex of five major small nuclear ribonucleoprotein particles (snRNPs). The pre-mRNA splicing factor PRPF8 is a crucial component of the U5 snRNP, and together with EFTUD2 and SNRNP200, it forms a central module of the spliceosome. Using quantitative proteomics, we identified assembly intermediates containing PRPF8, EFTUD2, and SNRNP200 in association with the HSP90/R2TP complex, its ZNHIT2 cofactor, and additional proteins. HSP90 and R2TP bind unassembled U5 proteins in the cytoplasm, stabilize them, and promote the formation of the U5 snRNP. We further found that PRPF8 mutants causing Retinitis pigmentosa assemble less efficiently with the U5 snRNP and bind more strongly to R2TP, with one mutant retained in the cytoplasm in an R2TP-dependent manner. We propose that the HSP90/R2TP chaperone system promotes the assembly of a key module of U5 snRNP while assuring the quality control of PRPF8. The proteomics data further reveal new interactions between R2TP and the tuberous sclerosis complex (TSC), pointing to a potential link between growth signals and the assembly of key cellular machines.

scholarly journals Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

2009 ◽  
Vol 8 (10) ◽  
pp. 2227-2242 ◽  
Author(s):  
Lily Ting ◽  
Mark J. Cowley ◽  
Seah Lay Hoon ◽  
Michael Guilhaus ◽  
Mark J. Raftery ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Quantitative Proteomics ◽  
Metabolic Labeling ◽  
Proteomics Data
Sign in / Sign up

Export Citation Format

Share Document