Halvade: scalable sequence analysis with MapReduce

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. We present Halvade, a framework that enables sequencing pipelin...

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Veröffentlicht in:Bioinformatics 2015-08, Vol.31 (15), p.2482-2488
Hauptverfasser: Decap, Dries, Reumers, Joke, Herzeel, Charlotte, Costanza, Pascal, Fostier, Jan
Format: Artikel
Sprache:eng
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Zusammenfassung: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. 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
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv179