FusionFlow: An Integrated System Workflow for Gene Fusion Detection in Genomic Samples

Federica Citarrella, Gianpaolo Bontempo, Marta Lovino, Elisa Ficarra

Abstract: Gene fusion is a genomic alteration where two genes after a break event are juxtaposed to form a new hybrid gene, leading to possible cancer development and progression. However, identifying gene fusions is not a trivial process as it requires the management and processing countless amounts of data. Genomic data (particularly DNA and RNA) can reach up to 300 GB per sample. Furthermore, specific software and hardware architectures are required to correctly process this type of data. Although many tools are available for detecting gene fusions, to date, systematic workflows that are free and easily usable even by non-specialists are hardly available. This paper presents an integrated system for identifying gene fusions in RNA and DNA genomic samples, focusing on hardware and software architectural aspects. The proposed workflow is easy-to-use, scalable, and highly reproducible. It includes five gene fusion detection tools, three mainly intended for RNA samples (EricScript, Arriba, FusionCatcher) and two for DNA samples (INTEGRATE and GeneFuse). The workflow runs on servers exploiting Nextflow (a DSL for data-driven computational pipelines), Docker containers, and Conda virtual environments.