Halvade: scalable sequence analysis with MapReduce

Dries Decap, Joke Reumers, Charlotte Herzeel, Pascal Costanza, Jan Fostier
2015 Bioinformatics  
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
more » ... 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, 50Â 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
doi:10.1093/bioinformatics/btv179 pmid:25819078 pmcid:PMC4514927 fatcat:2nmppry6nnfqrjhdljpukm5r3a