Detection of a fusion gene using soft-clipping reads in exome-sequencing data

A gene fusion plays an important role in oncogenes that drive tumor formation and progression because it can produce active abnormal protein. To use next generation sequencing (NGS) technologies, a different fusion gene has been discovered in human cancers. Many computational methods are developed by RNA-Seq data, and a few WGS-Seq data for fusion gene discovery but Exome-Seq data has not yet seen any use. We developed the new algorithm for detection of the fusion gene to use Exome-Seq data. In this approach, we first found candidate of fusion region to use soft-clipping reads, and the split reads are used for mapping fusion partner. Finally, the predicted fusion sequence was estimated for the frequency in read alignment. It is impossible to detect a fusion of introns with Exome-seq data that are far away from exon boundary. We were able to predict fusion boundary within exon that was caused by structural variations of the genome such as chromosomal translocation, deletion and inversion by aligning the sequence to the pseudo-reference from fusion sequence. In addition, the allele frequency information of fusions could be very useful to filter them and discover the biology from the genomic aberration, especially fusion gene in samples with heterogeneity like tumor tissue.