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Related Experiment Video

Updated: Dec 27, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation.

Emmanuel L P Dumont1, Benjamin Tycko1,2,3, Catherine Do1

  • 1Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ 07110, USA.

Bioinformatics (Oxford, England)
|March 3, 2020
PubMed
Summary
This summary is machine-generated.

CloudASM offers an efficient solution for quantifying allele-specific CpG methylation imbalance genome-wide. This novel pipeline overcomes the quadratic time complexity of previous methods, enabling broader practical application.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantifying allele-specific CpG methylation imbalance genome-wide is crucial for understanding epigenetic regulation.
  • Existing methods suffer from quadratic time complexity and require significant infrastructure management.

Purpose of the Study:

  • To develop a scalable and efficient pipeline for genome-wide allele-specific CpG methylation analysis.
  • To overcome the computational and infrastructural limitations of current methods.

Main Methods:

  • Development of CloudASM, a novel pipeline manager.
  • Leveraging Google Cloud Platform (GCP) serverless enterprise data warehouse.
  • Implementation of an ultra-efficient, turn-key, and portable workflow.

Main Results:

  • CloudASM achieves significantly improved efficiency compared to existing methods.
  • The pipeline is scalable and portable, simplifying complex analyses.
  • Provides a practical solution for genome-wide methylation imbalance quantification.

Conclusions:

  • CloudASM democratizes genome-wide allele-specific CpG methylation analysis.
  • Enables researchers to perform complex epigenetic analyses with reduced computational burden.
  • Facilitates broader adoption of advanced epigenetic studies.