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Streaming support for data intensive cloud-based sequence analysis
Shadi A Issa1, Romeo Kienzler, Mohamed El-Kalioby
1Center for Informatics Sciences, Nile University, Giza, Egypt.
Biomed Research International
|May 28, 2013
Summary
Cloud computing offers scalable genomics analysis, but data transfer is slow. This study introduces a streaming approach to process next-generation sequencing (NGS) data during transfer, reducing latency and costs.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Next-generation sequencing (NGS) generates massive datasets, creating a data deluge challenge.
- Cloud computing offers scalable, cost-effective resources for genomics data analysis.
- Large NGS data sizes lead to significant transfer latency, hindering cloud adoption.
Purpose of the Study:
- To address the data transfer latency bottleneck in cloud-based genomics analysis.
- To develop a method for processing NGS data concurrently with its transfer to the cloud.
- To provide a practical solution for efficient cloud utilization in genomics.
Main Methods:
- A streaming-based scheme for processing NGS data during transfer.
- Development of the elastream package to support the scheme.
- Integration with individual analysis programs and workflow systems.
Main Results:
- The proposed scheme effectively mitigates data transfer latency.
- Significant time savings in NGS data analysis using cloud resources.
- Reduced computational costs due to efficient data handling.
Conclusions:
- Streaming-based processing is a viable solution for cloud-based NGS data analysis.
- The elastream package facilitates the implementation of this scheme.
- This approach enhances the accessibility and efficiency of cloud computing for genomics research.
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