Fusion Gene Detection with Soft-Clipping Reads from Exome Sequencing Data

Next-generation sequencing (NGS) technology allows us to detect fusion genes from genome and transcriptome. Fusion genes play an important role in oncogenes that drive tumor formation and progression because it can produce active abnormal protein. Many computational methods to discover fusion genes are developed by mRNA sequencing data or whole genome sequencing data. But there are no gene fusion tools based on whole exome sequencing (WES) data. WES data is useful to contain functional regions of gene and to study various variations economically. Hence, we represented the new algorithm for detecting fusion genes with WES 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 genomic aberration from biology, especially fusion gene in samples with heterogeneity like tumor tissue.