Identification of novel transcripts in hepatocellular carcinoma using RNA-seq

Abstract

Recent advances in sequencing technologies have helped the finding of novel transcripts by deep RNA sequencing (RNA-Seq). The approach to identify novel transcripts using RNA-Seq has been studied, however, prediction of their functional roles in cancers are not studied thoroughly. Here, I constructed new pipeline to identify putative novel transcripts which might play functional roles in hepatocellular carcinoma (HCC) using reference based assembly method, which revealed 20 novel candidate transcripts. With those candidate transcripts, I predicted whether they have functions involved in cancer progression. Firstly, I identified function ontology for high sequence similarity gene sets of each novel transcript. Similarity genes had significant enriched function in regulation of transcription and regulation of RNA metabolic process. And I confirmed that the expressions of the candidate genes were correlated with those of the genes with sequence similarity. Secondly, putative binding proteins were identified by applying a prediction algorithm for RNA-RNA interaction. Collectively, I found the putative transcripts which might have functional roles in cancer progression by showing the functional enrichment of their putative targets genes with correlated expression, binding potential, and sequence similarity. In this study, new pipeline detected novel transcripts in HCC that may have functional roles. This approach will apply to further study to predict functional role of novel transcript in cancer.