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

ABSTRACT

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.