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Transcriptomics and epigenetic data integration learning module on Google Cloud.

Nathan A Ruprecht1, Joshua D Kennedy1,2, Benu Bansal1

  • 1Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States.

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Summary
This summary is machine-generated.

This study introduces a Google Cloud-based learning module for integrating transcriptomics and epigenetics data analysis. It provides tutorials for researchers to perform multi-omics analysis, aiding in cancer research and precision medicine.

Keywords:
DNA methylationGoogle Cloud computingR Bioconductorepigenomicsmulti-omics integrationtranscriptomics

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Area of Science:

  • Computational Biology and Bioinformatics
  • Genomics and Epigenomics
  • Cancer Research and Precision Medicine

Background:

  • Multi-omics approaches are crucial for understanding complex human biology, particularly in cancer research and precision medicine.
  • Advances in high-throughput sequencing and computational resources enable integrated analysis of diverse molecular data.
  • Efficiently analyzing large biological datasets requires proficiency in advanced tools like Google Cloud Platform.

Purpose of the Study:

  • To develop a guided learning module for integrating transcriptomics and epigenetics data analysis protocols.
  • To provide researchers with a comprehensive pipeline on Google Cloud for multi-omics data analysis.
  • To facilitate the identification of biomarkers and therapeutic targets for personalized medicine.

Main Methods:

  • Development of a three-submodule learning module utilizing Google Cloud Platform infrastructure.
  • Tutorials cover RNA-sequence (transcriptomics) and Reduced-Representation Bisulfite Sequencing (epigenetics) data analysis.
  • Analysis performed in Vertex AI Jupyter notebooks with R kernel, using breast cancer case studies from Gene Expression Omnibus.

Main Results:

  • The module guides users from data collection and preprocessing to downstream analysis and results visualization.
  • Analysis results are stored in Google Cloud buckets, offloading computational burden from local resources.
  • Provides a start-to-finish tutorial for researchers with limited multi-omics experience.

Conclusions:

  • The developed learning module empowers researchers to integrate transcriptomics and epigenetics data analysis effectively.
  • This resource supports the advancement of multi-omics research in cancer and other complex diseases.
  • The module is part of the NIGMS Sandbox for Cloud-based Learning, promoting cloud-based bioinformatics education.