Computational identification of specific splicing regulatory elements from RNA-seq in lung cancer.

BACKGROUND Lung cancer is the most common cause of cancer-related death worldwide. Recently, deep transcriptional sequencing has been used as an effective genomic assay to get an insight into this disease. AIM This study is carried out to identify specific regulatory elements (SREs) in lung cancer. MATERIALS AND METHODS The RNA-sequencing data on lung cancer sample and normal sample were downloaded from NCBI. TopHat and Cufflinks were used to analyze differential alternative splicing in lung cancer by using RNA-sequencing data. Further, we searched specific SREs in lung cancer through finding over-represented hexamers around high expression exons. RESULTS According to the Jensen-Shannon divergence between two samples and the p-value of t-test, we found 53 genes with differential alternative splicing in lung cancer. In the analysis of SREs, we found 763 specific SREs between lung cancer sample and normal sample. CONCLUSIONS These results may give an insight into how alternative splicing causes differential expression in lung cancer.