期刊
BIOINFORMATICS
卷 31, 期 15, 页码 2482-2488出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv179
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资金
- Intel
- Janssen Pharmaceutica
- Institute of the Promotion of Innovation through Science and Technology in Flanders (IWT)
- Ghent University (Multidisciplinary Research Partnership 'Bioinformatics: From Nucleotides to Networks')
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, 50x 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.
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