Cloudbreak: Accurate and Scalable Genomic Structural Variation Detection in the Cloud with MapReduce
Abstract
The detection of genomic structural variations (SV) remains a difficult challenge in analyzing sequencing data, and the growing size and number of sequenced genomes have rendered SV detection a bona fide big data problem. MapReduce is a proven, scalable solution for distributed computing on huge data sets. We describe a conceptual framework for SV detection algorithms in MapReduce based on computing local genomic features, and use it to develop a deletion and insertion detection algorithm, Cloudbreak. On simulated and real data sets, Cloudbreak achieves accuracy improvements over popular SV detection algorithms, and genotypes variants from diploid samples. It provides dramatically shorter runtimes and the ability to scale to big data volumes on large compute clusters. Cloudbreak includes tools to set up and configure MapReduce (Hadoop) clusters on cloud services, enabling on-demand cluster computing. Our implementation and source code are available at http://github.com/cwhelan/cloudbreak .
Cite
@article{arxiv.1307.2331,
title = {Cloudbreak: Accurate and Scalable Genomic Structural Variation Detection in the Cloud with MapReduce},
author = {Christopher W. Whelan and Jeffrey Tyner and Alberto L'Abbate and Clelia Tiziana Storlazzi and Lucia Carbone and Kemal Sönmez},
journal= {arXiv preprint arXiv:1307.2331},
year = {2013}
}
Comments
16 Pages main paper, 3 main figures, 2 main tables; 27 pages supplementary material including 13 page user manual, 5 supplementary figures. Code available at http://github.com/cwhelan/cloudbreak . Update: removed trailing period in abstract URL that was breaking link to GitHub; fixed colors in main figures