scholarly journals mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

2015 ◽  
Vol 129 ◽  
pp. 108-120 ◽  
Author(s):  
Guoshou Teo ◽  
Sinae Kim ◽  
Chih-Chiang Tsou ◽  
Ben Collins ◽  
Anne-Claude Gingras ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Analysis ◽  
Quantitative Proteomics ◽  
Proteomics Data ◽  
Data Independent Acquisition

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 NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals Sensitive Quantitative Proteomics of Human Hematopoietic Stem and Progenitor Cells by Data-independent Acquisition Mass Spectrometry

2019 ◽  
Vol 18 (7) ◽  
pp. 1454-1467 ◽  
Author(s):  
Sabine Amon ◽  
Fabienne Meier-Abt ◽  
Ludovic C. Gillet ◽  
Slavica Dimitrieva ◽  
Alexandre P. A. Theocharides ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Progenitor Cells ◽  
Quantitative Proteomics ◽  
Hematopoietic Stem ◽  
Data Independent Acquisition ◽  
Stem And Progenitor Cells

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

scholarly journals Implementing the re-use of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

2021 ◽  
Author(s):  
Mathias Walzer ◽  
David Garcia-Seisdedos ◽  
Ananth Prakash ◽  
Paul Brack ◽  
Peter Crowther ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Analysis ◽  
Free Software ◽  
Analysis Software ◽  
Analysis Pipeline ◽  
The Public ◽  
Data Independent Acquisition ◽  
Metadata Annotation ◽  
Expression Atlas ◽  
Quantitative Results

Rising numbers of mass spectrometry proteomics datasets available in the public domain, increasingly include volumes generated from Data Independent Acquisition approaches, SWATH-MS in particular. Unlike Data Dependent Acquisition datasets, their re-use is limited, partially due to challenges in combination and use of free software for analysis in the non-specialist laboratory. We introduce a (re-)analysis pipeline for SWATH-MS data available in the PRIDE database, which includes a harmonised combination of metadata annotation protocols, automated workflows for MS data, statistical analysis and results integration into the resource Expression Atlas. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available, reproducible and easy to update. To demonstrate its utility, we reanalysed 10 public DIA datasets, 1,278 individual SWATH-MS runs, stored in PRIDE. The robustness of the analysis was evaluated and compared to the results obtained in the original publications. The final results were exported into Expression Atlas, making quantitative results from SWATH-MS experiments more widely available and integrated with results from other reanalysed proteomics and transcriptomics datasets.

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