DoGFinder: a software for the discovery and quantification of readthrough transcripts from RNA-seq

Yuval Wiesel, Niv Sabath, Reut Shalgi

Recent studies have described a widespread induction of transcriptional readthrough as a consequence of various stress conditions in mammalian cells. This novel phenomenon, initially identified from analysis of RNA-seq data, suggests intriguing new levels of gene expression regulation. However, the mechanism underlying naturally occurring transcriptional readthrough, as well as its regulatory consequences, still remain elusive. Furthermore, the readthrough response to stress has thus far not been investigated outside of mammalian species, and the occurrence of readthrough in many physiological and disease conditions remains to be explored. To facilitate a wider investigation into transcriptional readthrough, we created the DoGFinder software package, for the streamlined identification and quantification of readthrough transcripts, also known as DoGs (Downstream of Gene-containing transcripts), from any RNA-seq dataset. Using DoGFinder, we explore the dependence of DoG discovery potential on RNA-seq library depth, and show that stress-induced readthrough induction discovery is robust to sequencing depth, and input parameter settings. We further demonstrate the use of the DoGFinder software package on a new publically available RNA-seq dataset, and discover DoG induction in human PME cells following hypoxia - a previously unknown readthrough inducing stress type. DoGFinder will enable users to explore, in a few simple steps, the readthrough phenomenon in any condition and organism. DoGFinder is freely available at https://github.com/shalgilab/DoGFinder .