Halvade
Computational Tool
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General Information
DescriptionScalable sequence analysis with MapReduce
Homepagehttp://bioinformatics.intec.ugent.be/halvade
Publications
Halvade: scalable sequence analysis with MapReduce
Dries Decap; Joke Reumers; Charlotte Herzeel; Pascal Costanza; Jan Fostier
Motivation: Post-sequencing DNA analysis typically consists of read mapping followed by variant calling. Especially for whole genome sequencing, this computational step is very time-consuming, even when using multithreading on a multi-core machine.
Results: We present Halvade, a framework that enables sequencing pipelines to be executed in parallel on a multi-node and/or multi-core compute infrastructure in a highly efficient manner. As an example, a DNA sequencing analysis pipeline for variant calling has been implemented according to the GATK Best Practices recommendations, supporting both whole genome and whole exome sequencing. Using a 15-node computer cluster with 360 CPU cores in total, Halvade processes the NA12878 dataset (human, 100 bp paired-end reads, 50× coverage) in <3 h with very high parallel efficiency. Even on a single, multi-core machine, Halvade attains a significant speedup compared with running the individual tools with multithreading.
Availability and implementation: Halvade is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR. Its source is available at http://bioinformatics.intec.ugent.be/halvade under GPL license.
Contact: jan.fostier@intec.ugent.be
Supplementary information: Supplementary data are available at Bioinformatics online.
DOI: https://doi.org/10.1093/bioinformatics/btv179
Metrics:
5
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