Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow

Abstract

In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture designed for running a resource-intensive aligner -- STAR -- and processing tens or hundreds of terabytes of RNA-sequencing data. We implement multiple optimization techniques that give significant execution time and cost reduction. The impact of particular optimizations is measured in medium-scale experiments followed by a large-scale experiment that leverages all of them and validates the current design. Early stopping optimization allows a reduction in total alignment time by 23%. We analyze the scalability and efficiency of one of the most widely used sequence aligners. For the cloud environment, we identify one of the most suitable EC2 instance types and verify the applicability of spot instances usage.