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

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 Workflow for Large-Scale Selected Reaction Monitoring Experiments

2012 ◽  
Vol 11 (3) ◽  
pp. 1644-1653 ◽  
Author(s):  
Lars Malmström ◽  
Johan Malmström ◽  
Nathalie Selevsek ◽  
George Rosenberger ◽  
Ruedi Aebersold
Keyword(s):  
Large Scale ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring

scholarly journals Interoperable and scalable data analysis with microservices: Applications in Metabolomics

2017 ◽  
Author(s):  
Payam Emami Khoonsari ◽  
Pablo Moreno ◽  
Sven Bergmann ◽  
Joachim Burman ◽  
Marco Capuccini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Large Scale ◽  
Metabolite Identification ◽  
Access Point ◽  
Scientific Discipline ◽  
Resonance Spectroscopy ◽  
Magnetic Resonance Spectroscopy Study ◽  
Analysis Workflow ◽  
Computational Resources

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

scholarly journals Quantitative assay of targeted proteome in tomato trichome glandular cells using a large-scale selected reaction monitoring strategy

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Ayako Takemori ◽  
Taiken Nakashima ◽  
Hisashi Ômura ◽  
Yuki Tanaka ◽  
Keisuke Nakata ◽  
...  
Keyword(s):  
Large Scale ◽  
Quantitative Assay ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Monitoring Strategy ◽  
Glandular Cells

Use of selected reaction monitoring data for label-free quantification of protein modification stoichiometry

PROTEOMICS ◽  
2010 ◽  
Vol 10 (23) ◽  
pp. 4301-4305 ◽  
Author(s):  
Deepa Balasubramaniam ◽  
Christie L. Eissler ◽  
Cynthia V. Stauffacher ◽  
Mark C. Hall
Keyword(s):  
Protein Modification ◽  
Monitoring Data ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Label Free ◽  
Label Free Quantification ◽  
Free Quantification

scholarly journals Representation of selected-reaction monitoring data in the mzQuantML data standard

PROTEOMICS ◽  
2015 ◽  
Vol 15 (15) ◽  
pp. 2592-2596 ◽  
Author(s):  
Da Qi ◽  
Craig Lawless ◽  
Johan Teleman ◽  
Fredrik Levander ◽  
Stephen W. Holman ◽  
...  
Keyword(s):  
Monitoring Data ◽  
Selected Reaction Monitoring ◽  
Reaction Monitoring ◽  
Data Standard

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 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 Interoperable and scalable data analysis with microservices: applications in metabolomics

2019 ◽  
Vol 35 (19) ◽  
pp. 3752-3760 ◽  
Author(s):  
Payam Emami Khoonsari ◽  
Pablo Moreno ◽  
Sven Bergmann ◽  
Joachim Burman ◽  
Marco Capuccini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Large Scale ◽  
Scientific Discipline ◽  
Supplementary Information ◽  
Resonance Spectroscopy ◽  
Research Environment ◽  
Metabolomics Data ◽  
Analysis Workflow ◽  
Virtual Research Environment

Abstract Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. Availability and implementation The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary information Supplementary data are available at Bioinformatics online.

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