AERON: Transcript quantification and gene-fusion detection using long reads

Single-molecule sequencing technologies have the potential to improve the measurement and analysis of long RNA molecules expressed in cells. However, the analysis of error-prone long RNA reads is a current challenge.

We present AERON for the estimation of transcript expression and prediction of gene-fusion events.

AERON uses an efficient read-to-graph alignment algorithm to obtain accurate estimates for noisy reads. We demonstrate AERON to yield accurate expression estimates on simulated and real datasets. It is the first method to reliably call gene-fusion events from long RNA reads. Sequencing the K562 transcriptome, we used AERON and found known as well as novel gene-fusion events.

Authors: Mikko Rautiainen, Dilip Durai, Ying Chen, Lixia Xin, Hwee Meng Low, Jonathan Goeke, Tobias Marschall, Marcel Schulz