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Updated: Jun 5, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Galaxy CloudMan: delivering cloud compute clusters.

Enis Afgan¹, Dannon Baker, Nate Coraor

¹Department of Biology, Emory University, Atlanta, GA 30322, USA.

BMC Bioinformatics

|January 8, 2011

Summary

This summary is machine-generated.

Researchers can now easily deploy custom cloud computing clusters for genomic analysis. This system simplifies access to computational power and biological tools, eliminating the need for extensive informatics expertise.

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Last Updated: Jun 5, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

High-throughput sequencing demands significant computational infrastructure.
Cloud computing offers flexible, on-demand resources but is often unsuitable for biologists.
Existing cloud solutions require substantial informatics knowledge.

Purpose of the Study:

To develop a user-friendly cloud resource management system for genomic research.
To enable experimental biologists to deploy and manage compute clusters without informatics expertise.
To provide immediate access to a suite of biological analysis tools.

Main Methods:

A novel cloud resource management system was developed.
The system leverages Amazon Elastic Compute Cloud (EC2) infrastructure.
Integration with the NERC Bio-Linux tool suite for immediate use.

Main Results:

Researchers can compose and control arbitrarily sized compute clusters.
A fully configured compute cluster is deployable in under five minutes via a web browser.
Automated methods for custom cloud resource deployment enhance reproducibility.

Conclusions:

The presented system removes barriers to cloud computing adoption for genomic research.
Researchers can access scalable computing power and analysis software on demand.
This facilitates handling large-scale genomic data and ensures reproducible results.