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 EUREKATAX: A Taxonomy for the Representation and Analysis of Qualitative Usability Test Data

2020 ◽  
Vol 4 (2) ◽  
pp. 22
Author(s):  
Panagiotis Germanakos ◽  
Ludwig Fichte
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Empirical Data ◽  
Formal Method ◽  
Real Life ◽  
Learning Activity ◽  
Perceived Fit ◽  
Usability Tests ◽  
Usability Research ◽  
The One

Usability tests serve as an insightful source of feedback for product teams that want to deliver user-centered solutions and enhance the User Experience (UX) of their products and services. However, in many cases, formative usability tests in particular may generate a large volume of qualitative and unstructured data that need to be analyzed for decision making and further actions. In this paper, we discuss a more formal method of analyzing empirical data, using a taxonomy, namely Engineering Usability Research Empirical Knowledge and Artifacts Taxonomy (EUREKATAX). We describe how it can provide guidance and openness for transforming fuzzy feedback statements into actionable items. The main aim of the proposed method is to facilitate a more holistic and standardized process to empirical data analysis while adapting on the solution or context. The main contributions of this work comprise the: (a) definition of the proposed taxonomy which represents an organization of information structured in a hierarchy of four main categories (discover, learn, act, and monitor), eight sub-categories, and 52 items (actions/operations with their respective properties); (b) description of a method, that is expressed through the taxonomy, and adheres to a systematic but modular approach for analyzing data collected from the usability studies for decision making and implementation; (c) formulation of the taxonomy’s theoretical framework based on meticulously selected principles like experiential learning, activity theory: learning by expanding, and metacognition, and (d) extended evaluation into two phases, with 80 UX experts and business professionals, showing on the one hand the strong reliability of the taxonomy and high perceived fit of the items in the various classifications, and on the other hand the high perceived usability, usefulness and acceptability of the taxonomy when put into practice in real-life conditions. These findings are really encouraging, in an attempt to generate comparable, generalizable and replicable results of usability tests’ qualitative data analysis, thereby improving the UX and impact of software solutions.

scholarly journals In-Depth Benchmarking of DIA-type Proteomics Data Analysis Strategies Using a Large-Scale Benchmark Dataset Comprising Inter-Patient Heterogeneity

2021 ◽  
Author(s):  
Klemens Fröhlich ◽  
Eva Brombacher ◽  
Matthias Fahrner ◽  
Daniel Vogele ◽  
Lucas Kook ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Statistical Tests ◽  
Benchmark Dataset ◽  
Clinical Samples ◽  
Proteomics Data ◽  
Data Independent Acquisition ◽  
Analysis Strategies ◽  
Spectral Libraries ◽  
Differentially Abundant Proteins

Abstract An overwhelming number of proteomics software tools and algorithms have been published for different steps of Data Independent Acquisition analysis of clinical samples. Nonetheless, there is still a lack of comprehensive benchmark studies evaluating which combinations of those isolated components perform best. Here, we used 92 lymph nodes from distinct patients to create a unique benchmark dataset representing real-world inter-individual heterogeneity. The publicly available dataset comprises 118 LC-MS/MS runs with > 12 million MS2 spectra and allowed us to objectively evaluate how well different combinations of spectral libraries, DIA software, sparsity reduction, normalization and statistical tests can detect differentially abundant proteins, while also taking sample size into account. Evaluation of 2 million data analysis workflows showed that a gas phase fractionation refined spectral library in combination with DIA-NN and Significance Analysis of Microarrays reliably detected differentially abundant proteins. Furthermore, DIA-NN and Spectronaut robustly avoided the false detection of truly absent proteins.

scholarly journals Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics

2019 ◽  
Author(s):  
Lindsay K Pino ◽  
Han-Yin Yang ◽  
William Stafford Noble ◽  
Brian C Searle ◽  
Andrew N Hoofnagle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Quantitative Proteomics ◽  
Protein Abundance ◽  
Complex Samples ◽  
Figures Of Merit ◽  
Data Independent Acquisition ◽  
Proteomics Experiment ◽  
Series Of Experiments ◽  
Analytical Figures Of Merit

AbstractMass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

scholarly journals Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

2021 ◽  
Author(s):  
Claudia Ctortecka ◽  
Gabriela Krššáková ◽  
Karel Stejskal ◽  
Josef M. Penninger ◽  
Sasha Mendjan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Expression ◽  
Single Cell ◽  
Missing Values ◽  
Expression Profiles ◽  
Single Cells ◽  
Proteomics Data ◽  
Biological Origin ◽  
Low Input ◽  
Data Independent Acquisition

AbstractSingle cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not take full advantage of isotope-encoded sample multiplexing. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin, and to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

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 Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

2021 ◽  
Author(s):  
Tom S. Smith ◽  
Anja Andrejeva ◽  
Josie A. Christopher ◽  
Oliver M. Crook ◽  
Mohamed A.W. Elzek ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Quantitative Data ◽  
Signal Acquisition ◽  
Tandem Mass ◽  
Proteomics Data ◽  
Mixed Species ◽  
Differential Abundance ◽  
Proteomics Experiment ◽  
The Impact ◽  
Acquisition Software

Tandem mass tags (TMT) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software, Tune. However, 53 % of Orbitrap Fusion, Lumos or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Sub-notch intensities are systemically underestimated with Tune version 2, leading to over-estimation of the true differences in intensities between samples. However, when summarising reporter ion intensities to higher level features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.

scholarly journals PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis

2018 ◽  
Vol 35 (5) ◽  
pp. 898-900 ◽  
Author(s):  
Cheng Chang ◽  
Mansheng Li ◽  
Chaoping Guo ◽  
Yuqing Ding ◽  
Kaikun Xu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Quantitative Proteomics ◽  
Proteomics Data ◽  
Flexible Tool ◽  
Proteomics 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 ◽  
Proteomics Data ◽  
Analysis Pipeline ◽  
Data Independent Acquisition ◽  
One Step ◽  
Automatic Tool

AbstractMotivationData mining and data quality evaluation are indispensable constituents of quantitative proteomics, but few integrated tools available.ResultsWe 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.AvailabilityobaDIA is freely available from https://github.com/yjthu/[email protected]

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