RNA-Seq analyses to reveal the human transcriptome landscape

Abstract

Alternative splicing plays important roles in many biological processes including diseases. It markedly increases the diversity of transcriptome and proteome since over 90% of human genes are alternatively spliced. Recently, the high-throughput RNA-Seq technology makes it possible to better characterize and understand transcriptomes. Differential expression and differential splicing are two fundamental yet crucial analyses to study differences between transcriptomes. The results from analyses may reveal the landscape of human transcriptomes and yield new insight into cell differentiation that may lead to human disease. We present the analysis results from two RNA-Seq data sets to study the transcriptomes of a human disease and a type of human cell differentiation. For the first study, we applied our analysis pipeline to a RNA-Seq data set of human Idiopathic Pulmonary Fibrosis (IPF) disease. We present a joint analysis result of differential expression and differential splicing to view genes from both aspects simultaneously. We also provide several non-differentially spliced genes with splicing variants validated by qRT-PCR experiments. For the second study, we developed a novel computational method, and applied it on a public RNA-Seq data set of human H1 and H1 differentiation into neural progenitor cell lines. We systematically detected many significant differential splicing events falling into five well-known types of alternative splicing. We present the proportion of the five types of detected differential splicing events in this study. For each type of splicing event, we show a case study to demonstrate the detection procedure of the differential splicing event.