Abstract

The evolution of technologies has unleashed a wealth of challenges by generating massive amount of data. Recently, biological data has increased exponentially, which has introduced several computational challenges. DNA short read alignment is an important problem in bioinformatics. The exponential growth in the number of short reads has increased the need for an ideal platform to accelerate the alignment process. Apache Spark is a cluster-computing framework that involves data parallelism and fault tolerance. In this article, we proposed a Spark-based algorithm to accelerate DNA short reads alignment problem, and it is called Spark-DNAligning. Spark-DNAligning exploits Apache Spark ’s performance optimizations such as broadcast variable, join after partitioning, caching, and in-memory computations. Spark-DNAligning is evaluated in term of performance by comparing it with SparkBWA tool and a MapReduce based algorithm called CloudBurst. All the experiments are conducted on Amazon Web Services (AWS). Results demonstrate that Spark-DNAligning outperforms both tools by providing a speedup in the range of 101–702 in aligning gigabytes of short reads to the human genome. Empirical evaluation reveals that Apache Spark offers promising solutions to DNA short reads alignment problem.