Statistical Methods for Transcriptome Profiling Using RNA Sequencing

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): A transcriptome represents all transcribed sequences in a given cell. Unlike a genome, which is static, the transcriptome can be quickly restructured by changing the rate of synthesis or decay of individual mRNAs in response to external environmental conditions. Tissue and cell specific transcriptomic changes during pathophysiological stress, in disease versus health and in response to drug therapies are of particular interest to investigators studying human diseases. RNA-Sequencing (RNA-Seq) is an emerging approach that allows a comprehensive analysis of the entire transcriptome in a high-throughput manner. With deep coverage and single nucleotide resolution, RNA-Seq provides a platform to determine differential expression of genes or isoforms, alternative splicing, non-coding RNAs, post-transcriptional modifications, and gene fusions. Although studies using RNA-Seq have altered our view of the extent and complexity of eukaryotic transcriptomic variations, like other high-throughput sequencing technologies, RNA-Seq faces several analytical challenges. Fully harvesting the power of this newly developed technique requires the development of effective statistical methods. Building upon our expertise in statistical methods development and experience with analysis of genomics data for complex human diseases, we propose to develop novel statistical methods that allow robust detection of transcriptomic variations. Our specific aims are to: 1) Develop statistical methods to analyze isoform-specific gene expression and alternative splicing. 2) Develop statistical methods to identify RNA editing events. 3) Apply the proposed methods to RNA-Seq data generated from ongoing collaborations on transcriptomics studies of experimental endotoxemia, heart failure, and age-related macular degeneration. 4) Develop open source software packages for methods proposed in this application. This proposal addresses critical analytical challenges regarding the analysis of RNA-Seq data. Our methods will make efficient use of existing RNA-Seq data generated from ongoing cardiovascular and ocular transcriptomics studies. The successful completion of this work will allow biologists to better disentangle complex cellular circuitry, precisely related genomic sequence to gene regulation, and facilitate the translation of basic research findings into clinical studies of cardiovascular and eye diseases.