Optimal Binning for Genomics

Abstract

Genome sequencing is expected to be the most prolific source of big data in the next decade; millions of whole genome datasets will open new opportunities for biological research and personalized medicine. Genome sequences are abstracted in the form of interesting regions, describing abnormalities of the genome. The parallel execution on the cloud of complex operations for joining and mapping billions of genomic regions is increasingly important. Genome binning, i.e., partitioning of the genome into small-size segments, adapts classic data partitioning methods to genomics; region distributions to bins must reflect operation-specific correctness rules. As a consequence, determining the optimal bin size for such operations is a complex mathematical problem, whose solution requires careful modeling. The main result of this paper is the mathematical formulation and solution of the optimal binning problem for join and map operations in the context of GMQL, a query language over genomic regions; the model is validated by experiments showing its accuracy and sensitivity to the variation of operations’ parameters. We also optimize sequences of operations by inheriting the binning between two consecutive operations and we show the deployment of GMQL and the tuning of the proposed model on different cloud computing systems.