Optimizing STAR Aligner for High Throughput Computing in the Cloud

Abstract

We propose a scalable, cloud-native architecture designed for Transcriptomics Atlas Pipeline, using a resource-intensive STAR aligner and processing tens or hundreds of terabytes of RNA-seq data. We implement the pipeline using AWS cloud services, introduce performance optimizations and perform experimental evaluation in the cloud. Our optimization techniques result in computational savings thanks to the "early stopping" approach, selection of right-sized resources, and using newer version of Ensembl genome.