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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

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Eoulsan: a cloud computing-based framework facilitating high throughput sequencing analyses.

Laurent Jourdren¹, Maria Bernard, Marie-Agnès Dillies

¹École normale supérieure, Institut de Biologie de l'ENS, INSERM U1024, Paris, France. eoulsan@biologie.ens.fr

Bioinformatics (Oxford, England)

|April 12, 2012

Summary

This summary is machine-generated.

We created Eoulsan, a scalable framework for analyzing high-throughput sequencing data. It automates sample analysis on cloud computing clusters, with costs and running times scaling linearly with data size and resources.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing generates massive datasets requiring efficient analysis.
Existing frameworks may lack scalability or ease of use for cloud environments.

Purpose of the Study:

To develop a modular and scalable framework for automated high-throughput sequencing data analysis.
To enable users to easily set up cloud computing clusters for genomic data processing.

Main Methods:

Developed Eoulsan, a framework based on the Hadoop MapReduce algorithm.
Implemented in Java and supported on Linux systems.
Tested on Amazon Web Services cloud infrastructure.

Main Results:

Eoulsan demonstrated modularity and scalability for high-throughput sequencing data analysis.
Cloud computation cost scaled linearly with the number of instances.
Running time scaled linearly with increasing data amounts.

Conclusions:

Eoulsan provides an efficient and cost-effective solution for large-scale genomic data analysis.
The framework facilitates automated analysis pipelines in cloud environments.
Its linear scalability ensures performance with growing datasets.