Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript.

Motivation: The discovery of novel gene fusions can lead to a better comprehension of cancer progression and development. The emer- gence of deep sequencing of trancriptome, known as RNA-seq, has opened many opportunities for the identification of this class of gen- omic alterations, leading to the discovery of novel chimeric transcripts in melanomas, breast cancers and lymphomas. Nowadays, few com- putational approaches have been developed for the detection of chi- meric transcripts. Although all of these computational methods show good sensitivity, much work remains to reduce the huge number of false-positive calls that arises from this analysis. Results: We proposed a novel computational framework, named chimEric tranScript detection algorithm (EricScript), for the identifica- tion of gene fusion products in paired-end RNA-seq data. Our simu- lation study on synthetic data demonstrates that EricScript enables to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. We also applied our method to pub- licly available RNA-seq tumour datasets, and we showed its capability in rediscovering known gene fusions. Availability: The EricScript package is freely available under GPL v3 license at http://ericscript.sourceforge.net.