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RNA-seq

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Halvade: scalable sequence analysis with MapReduce.

Dries Decap¹, Joke Reumers², Charlotte Herzeel³

¹Department of Information Technology, Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium, ExaScience Life Lab, Kapeldreef 75, 3001 Leuven, Belgium.

Bioinformatics (Oxford, England)

|March 31, 2015

Summary

This summary is machine-generated.

Halvade is a new framework that significantly speeds up DNA sequencing analysis, including variant calling for whole genome sequencing. It efficiently utilizes multi-node and multi-core systems for faster results.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA sequencing analysis involves computationally intensive steps like read mapping and variant calling.
These processes are particularly time-consuming for whole genome sequencing, even with multithreading.

Purpose of the Study:

To introduce Halvade, a novel framework designed to enhance the efficiency of DNA sequencing pipelines.
To enable parallel execution of sequencing tasks on distributed compute infrastructures.

Main Methods:

Implementation of a DNA sequencing analysis pipeline for variant calling following GATK Best Practices.
Utilizing Halvade for parallel processing on a multi-node, multi-core compute cluster.

Main Results:

Halvade processed the NA12878 dataset (whole genome sequencing) in under 3 hours on a 15-node cluster (360 CPU cores).
Achieved very high parallel efficiency, demonstrating significant speedup even on a single multi-core machine compared to traditional multithreaded tools.

Conclusions:

Halvade offers a highly efficient solution for accelerating DNA sequencing analysis pipelines.
The framework provides substantial performance improvements for variant calling in both whole genome and whole exome sequencing.