Big Data Approaches for Clinical RNA Sequencing (RNA-Seq)

• Activity Number: 320
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Shihao Shen*
• Companies: UCLA
• Keywords: RNA sequencing ; clinical data ; transcriptome ; alternative splicing

Abstract

The RNA sequencing (RNA-Seq) technology has become a powerful tool for the quantitative analyses of transcriptome isoform signatures related to human diseases. With the rapid reduction of sequencing cost, it has become feasible to use in-depth RNA-Seq data to profile the transcriptome landscape of massive clinical cohorts. Tens of thousands of patients are being profiled each year for their genome-wide transcriptome features related to disease development and treatment strategy. Comprehensive RNA-Seq profiles of individuals lay the foundation for personalized treatments that target the underlying cause of disease. Despite the potential of clinical RNA sequencing, currently there is a significant lack of statistical models and software to accurately extract disease-related transcriptome signatures from large-scale RNA-Seq datasets. In this talk, I will discuss our recent efforts in developing statistical and computational approaches for elucidating the transcriptome isoform complexity of human diseases using massive RNA-Seq datasets.