A graphical, interactive and GPU-enabled workflow to process long-read sequencing data

Abstract

ABSTRACTWe present a graphical cloud-enabled workflow for fast, interactive analysis of nanopore sequencing data using GPUs. Users customize parameters, monitor execution and visualize results through an accessible graphical interface. To facilitate reproducible deployment, we use Docker containers and provide an Amazon Machine Image (AMI) with all software and drivers pre-installed for GPU computing on the cloud. We observe a 34x speedup and a 109x reduction in costs for the rate-limiting basecalling step in the analysis of blood cancer cell line data. The graphical interface and greatly simplified deployment facilitate the adoption of GPUs for rapid, cost-effective analysis of long-read sequencing.