SparkFlow : towards high-performance data analytics for Spark-based genome analysis
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The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatic data. However, this complicated process has become the system bottleneck, requiring an amalgamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., BaseRecalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and processor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper’s findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.
Filgueira , R , Awaysheh , F M , Carter , A , White , D J & Rana , O 2022 , SparkFlow : towards high-performance data analytics for Spark-based genome analysis . in 20252 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) . IEEE , pp. 1007-1016 , Workshop on Clusters, Clouds and Grids for Life Sciences (CCGrid Life 2022) , Taormina , Italy , 16/05/22 . https://doi.org/10.1109/CCGrid54584.2022.00123workshop
20252 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
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