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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Interactive Web Tool for Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry Data.

Sudhir Srivastava1,2, Michael Merchant3,4, Anil Rai1

  • 1Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.

Journal of Proteomics & Bioinformatics
|March 10, 2020
PubMed
Summary
This summary is machine-generated.

Standardizing proteomics workflows is crucial for reliable results. A new web application, the Proteomics Workflow Standardization Tool (PWST), helps researchers select optimal experimental steps by minimizing technical variability and handling missing data.

Keywords:
Coefficient of variationImputationPeptidesProteinsSum of squaresTechnical variability

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Proteomics experiments involve multiple steps with numerous choices, necessitating workflow standardization for robust experimental design.
  • Quantitative liquid chromatography-mass spectrometry measurements face challenges like technical variability and missing data, impacting data reliability.

Purpose of the Study:

  • To introduce a web application, Proteomics Workflow Standardization Tool (PWST), for standardizing proteomics experimental workflows.
  • To assist researchers in selecting optimal choices for each experimental step by identifying those with minimal variability.

Main Methods:

  • PWST utilizes statistical methods, including general linear models, analysis of covariance, and analysis of variance, to assess variability.
  • The tool analyzes data at both protein and peptide levels, accommodating missing values and calculating the Coefficient of Variation (CV).

Main Results:

  • Demonstration of PWST on data with categorical and continuous variables, highlighting its utility in variability assessment.
  • The application provides options to determine the contribution of sum of squares for each variable and the CV.

Conclusions:

  • PWST offers a valuable resource for standardizing proteomics workflows, thereby improving the reliability of quantitative measurements.
  • The web application is freely accessible and implemented in R as a Shiny application.