Transcriptome profiling by high throughput RNA Sequencing

描述(申请人提供)：大规模平行cDNA测序(RNA-SEQ)的最新进展为全面分析转录组铺平了道路，转录组是一组所有的RNA分子，包括一个或多个细胞群体中的mRNA、rRNA、tRNA和其他非编码RNA。RNA-Seq可以识别转录边界的精确位置，显示外显子是如何连接的，并揭示转录区域的序列变异。综上所述，RNA-Seq是第一种基于测序的方法，允许以非常高的吞吐量和定量的方式调查整个Transciptome，为注释和基因组规模的数字基因表达水平提供单碱基分辨率。然而，这项技术正在积极开发中，在文库准备过程中有几个酶步骤可能会导致序列依赖偏差，阻碍基因组区域之间的比较，并对跨染色体组图谱产生不利影响。用于在RNA-Seq中添加适配序列的连接酶具有基于结构的偏好，使第一条cDNA链易于复制错误和重排的逆转录酶，用于扩增的聚合酶在接近GC/AT富集区时经常出错。该项目的目标是创建一个文库准备试剂盒，用于制作最小偏差、高索引的RNA-Seq文库，用于深度测序，其构建方式允许转录组图谱易于和公平地询问。我们提议开发技术，以公正、高通量的方式研究转录组，应该会使未来的临床应用成为现实，并推动比较组织疾病图谱的研究，进一步解锁转录调控。

DESCRIPTION (provided by applicant): Recent advances in massively parallel cDNA sequencing (RNA-seq) have paved the way for comprehensive analysis of the transcriptome, a set of all RNA molecules including mRNA, rRNA, tRNA and other non-coding RNAs in one or more populations of cells. RNA-Seq can identify the precise location of transcription boundaries, show how exons are connected and reveal sequence variations in transcribed regions. Taken together, RNA-Seq is the first sequencing based method that allows the entire transciptome to be surveyed in a very high- throughput and quantitative manner, offering both single-base resolution for annotation and digital gene expression levels at the genome scale. However, this technology is under active development and there are several enzymatic steps during library preparation that can contribute to sequence dependent bias, hindering comparisons between genomic regions and adversely affecting transriptome profiling. Ligases used to add on adapter sequences in RNA- Seq have structure based preferences, reverse transcriptases that make the first cDNA strand are prone to copy errors and rearrangements, and polymerases used for amplification often stumble when approaching GC / AT rich regions. The goal of this project is to create a library preparation kit for making minimally biased, highly indexed RNA-Seq libraries for deep sequencing that are constructed in a way to allow transcriptome profiles to be easily and fairly interrogated. Our proposal to develop technology to study the transcriptome in an unbiased, high-throughput manner should make future clinical applications a reality and propel research in comparative tissue disease profiles, further un-locking transcriptional regulation.