scholarly journals SRMBUILDER: A USER-FRIENDLY TOOL FOR SELECTED REACTION MONITORING DATA ANALYSIS

2011 ◽  
Vol 09 (supp01) ◽  
pp. 51-62 ◽  
Author(s):  
QUANHU SHENG ◽  
CHAOCHAO WU ◽  
ZHIDUAN SU ◽  
RONG ZENG
Keyword(s):  
Data Analysis ◽  
Software Tool ◽  
High Sensitivity ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Post Translational Modification ◽  
Research Areas ◽  
File Formats ◽  
Data Files ◽  
User Friendly

With high sensitivity and reproducibility, selected reaction monitoring (SRM) has become increasingly popular in proteome research for targeted quantification of low abundance proteins and post translational modification. SRM is also well accepted in other mass-spectrometry based research areas such as lipidomics and metabolomics, which necessitates the development of easy-to-use software for both post-acquisition SRM data analysis and quantification result validation. Here, we introduce a software tool SRMBuilder, which can automatically parse SRM data in multiple file formats, assign transitions to compounds, match light/heavy transition/compound pairs and provide a user-friendly graphic interface to manually validate the quantification result at transition/compound/sample level. SRMBuilder will greatly facilitate processing of the post-acquisition data files and validation of quantification result for SRM. The software can be downloaded for free from as part of the software suite ProteomicsTools.

scholarly journals Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms

2016 ◽  
Author(s):  
Hannes L Röst ◽  
Ruedi Aebersold ◽  
Olga T Schubert
Keyword(s):  
Data Analysis ◽  
High Accuracy ◽  
Error Rates ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Specific Mass ◽  
Quantitative Accuracy ◽  
Analysis Workflow ◽  
Control Of Error ◽  
Automated Data Analysis

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze in a targeted manner data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility and quantitative accuracy.

Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies

2013 ◽  
Vol 8 (8) ◽  
pp. 1602-1619 ◽  
Author(s):  
Silvia Surinova ◽  
Ruth Hüttenhain ◽  
Ching-Yun Chang ◽  
Lucia Espona ◽  
Olga Vitek ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Monitoring Data ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Analysis Workflow

QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow

2020 ◽  
Vol 20 (1) ◽  
pp. 1096-1102
Author(s):  
Dazheng Wang ◽  
Guohong Gan ◽  
Xi Chen ◽  
Chuan-Qi Zhong
Keyword(s):  
Data Analysis ◽  
Software Tool ◽  
User Friendly

Mass spectrometry-based proteomics in biomedical research: emerging technologies and future strategies

2010 ◽  
Vol 12 ◽  
Author(s):  
Geraldine M. Walsh ◽  
Jason C. Rogalski ◽  
Cordula Klockenbusch ◽  
Juergen Kast
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Biomedical Research ◽  
Biological Systems ◽  
Basic Research ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Peptide Separation ◽  
Standard Methods ◽  
Biochemical Fractionation

In recent years, the technology and methods widely available for mass spectrometry (MS)-based proteomics have increased in power and potential, allowing the study of protein-level processes occurring in biological systems. Although these methods remain an active area of research, established techniques are already helping answer biological questions. Here, this recent evolution of MS-based proteomics and its applications are reviewed, including standard methods for protein and peptide separation, biochemical fractionation, quantitation, targeted MS approaches such as selected reaction monitoring, data analysis and bioinformatics. Recent research in many of these areas reveals that proteomics has moved beyond simply cataloguing proteins in biological systems and is finally living up to its initial potential – as an essential tool to aid related disciplines, notably health research. From here, there is great potential for MS-based proteomics to move beyond basic research, into clinical research and diagnostics.

scholarly journals A proteomics sample metadata representation for multiomics integration, and big data analysis.

2021 ◽  
Author(s):  
Chengxin Dai ◽  
Anja Fullgrabe ◽  
Julianus Pfeuffer ◽  
Elizaveta Solovyeva ◽  
Jingwen Deng ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Interactions ◽  
Ad Hoc ◽  
Data File ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Related Information ◽  
File Formats ◽  
Data Files ◽  
Public Datasets

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGE-TAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

Justifying the data analytical choice in single case research in relation to the expected data pattern

2019 ◽  
Author(s):  
Rumen Manolov
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Multilevel Models ◽  
Single Case ◽  
Analytical Techniques ◽  
Real Data ◽  
Small Scale ◽  
Data User ◽  
Single Case Research ◽  
User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

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