ASHG19 - Integration of Best Practice RNA-Seq Workflows into Cloud-Based Translational Analysis Platform
Micah Rickles-Young, Junko Tsuji, Alyssa Macbeth, Brian R. Granger, Tera Bowers, Carrie Cibulskis, Niall Lennon
The Broad Institute has a long history in genomic sequencing and in the development of tools for researchers to analyze these data. With improvements in technology and reductions in cost, the rate of sequence generation is increasing, which necessitates a platform to scale the associated analyses. We also need to be able to apply our best practice methods across a range of complex workflows to support the breadth of science among our users. This challenge is what spurred the creation of the Translational Analysis Group (TAG) within the Genomics Platform at the Broad Institute. Over the past two years, our group has developed and maintained over 30 validated, version-controlled workflows and has run over 20,000 analyses on Terra, the Broad Institute’s cloud-based analysis platform. Until recently, we have mainly focused on supporting germline and somatic variant analyses on whole genome and exome libraries, however there is high demand to integrate RNA-sequencing (RNA-seq) into the analyses. In this presentation, we introduce our new RNA-seq workflows for bulk and single-cell RNA experiments. Our suite of RNA-seq workflows starts with mapping RNA reads to a reference genome and then profiles gene and the isoform expression. The bulk RNA-seq outputs can be used as inputs for the downstream workflows to perform differential expression and RNA variant calling analysis. For evaluating the workflows, we benchmarked with publicly available datasets such as GTEx to check the expression and the RNA variant calls against the matched exome. The development of our RNA-seq analysis capabilities increases the scope of projects, both internal and external, for which TAG can provide analysis services with the reproducibility, scalable resources, and version control necessary for consistency in studies which extend over a long period of time, such as clinical trials.