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  1. Home
  3. Understanding Proteome Quantification In An Interactive Learning Module On Google Cloud Platform.
  1. Home
  3. Understanding Proteome Quantification In An Interactive Learning Module On Google Cloud Platform.

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Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

Kyle A O'Connell1,2, Benjamin Kopchick1,2, Thad Carlson1,2

  • 1Center for Information Technology, National Institutes of Health, 9000 Rockville Pike, Bethesda MD, 20892, United States.

Briefings in Bioinformatics
|July 23, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an interactive cloud-based learning module for quantitative proteomics, utilizing Google Cloud for accessible mass spectrometry data analysis. It covers protein quantification, normalization, and differential abundance analysis using R programming for researchers.

Keywords:
Google Cloud Platformmass spectrometryproteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative proteomics is a rapidly advancing field driven by high-resolution mass spectrometry.
  • Analyzing diverse proteomic data types (e.g., DDA, DIA, TMT) requires specialized knowledge in normalization, statistical design, and programming.
  • Existing learning resources may lack interactive, cloud-based platforms for practical data analysis training.

Purpose of the Study:

  • To develop an interactive learning module for quantitative proteomics analysis.
  • To provide accessible training on mass spectrometry data analysis using cloud resources.
  • To introduce researchers to protein quantification, normalization, and differential abundance analysis.

Main Methods:

  • Development of a cloud-based learning module within the NIGMS Sandbox platform.
  • Utilizing Google Cloud for interactive data access and analysis.
  • Demonstration of TMT10plex data analysis in a Jupyter notebook with an R kernel, including normalization and differential abundance analysis with limma models.

    • Main Results:

    • The module facilitates learning of mass spectrometry terminology, normalization methods, and R programming basics.
    • Learners gain practical experience navigating Google Cloud Platform for proteomic research.
    • The module effectively demonstrates end-to-end proteomic data analysis from raw intensities to differential abundance.

    Conclusions:

    • The NIGMS Sandbox learning module offers an accessible and interactive approach to quantitative proteomics.
    • Cloud-based platforms enhance the accessibility of complex data analysis training.
    • This resource empowers researchers with essential skills for modern proteomic data interpretation.