Optimizing STAR Aligner for High Throughput Computing in the Cloud

TL;DR

This paper presents a scalable cloud-native architecture for RNA-seq data alignment using STAR, optimizing performance and cost-efficiency through resource management and software improvements.

Contribution

It introduces a cloud-based pipeline for high-throughput RNA-seq alignment with novel optimization techniques and resource strategies.

Findings

Significant computational savings achieved

Effective use of AWS cloud services demonstrated

Performance improvements through early stopping and resource tuning

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.