Efficient alignment of next generation sequencing data using MapReduce on the cloud

This paper presents a methodology for running NGS read mapping tools in the cloud environment based on the MapReduce programming paradigm. As a demonstration, the recently developed and robust sequence alignment tool, BFAST, is used within our methodology to handle massive datasets. The results of our experiments show that the transformation of existing read mapping tools to run within the MapReduce framework dramatically reduces the total execution time and enables the user to utilize the resources provided by the cloud. © 2012 IEEE.