CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation

Emmanuel L P Dumont; Benjamin Tycko; Catherine Do

doi:10.1093/bioinformatics/btaa149

CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation

Bioinformatics. 2020 Jun 1;36(11):3558-3560. doi: 10.1093/bioinformatics/btaa149.

Authors

Emmanuel L P Dumont¹, Benjamin Tycko^{1

2

3}, Catherine Do¹

Affiliations

¹ Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ 07110, USA.
² Hackensack-Meridian Health School of Medicine at Seton Hall University, Nutley, NJ 07110, USA.
³ Lombardi Comprehensive Cancer, Center Georgetown University, Washington, DC 20007, USA.

Abstract

Summary: Methods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Platform (GCP) that uses a novel pipeline manager and GCP's serverless enterprise data warehouse.

Availability and implementation: CloudASM is freely available in the GitHub repository https://github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https://console.cloud.google.com/storage/browser/cloudasm.

Contact: [email protected].

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Alleles
Cloud Computing
DNA Methylation*
Software*

Grants and funding

R21 AI133140/AI/NIAID NIH HHS/United States