Quantitative and function analysis platform for repetitive genes and gene isoforms in pluripotency regulation and differentiations

项目摘要/摘要 许多研究表明，重复基因和基因异构体的存在及其重要功能， 特别是在干细胞生物学和发育生物学方面。虽然现有技术的发展 基于下一代测序(NGS)的特征转录组，极大地加速了 对不同转录事件的研究，并导致了许多重要的生物学发现，丰度 对重复基因和基因异构体的估计仍然是一个具有挑战性的问题。因此，许多下游 这阻碍了诸如差异表达分析和网络构建等定量分析 限制。由于新的长读技术已经被优化以传送强大的转录组测序数据 通过更明确的比对，它带来了新的可识别的信息，这些信息对解决某些 具有挑战性但重要的转录问题。我们的目标是发展一系列生物信息学 方法对重复基因和基因异构体进行更可靠的定量和功能分析， 包括丰度估算、网络结构和功能预测。目标1是确定量化 错误和不正确量化的基因和基因异构体。目标二是解决量化的问题 通过数据集成。目的3是构建基因异构体网络，寻找可能的异构体特异性功能 通过网络分析。这些方法将用于研究重复基因的表达和功能以及 人类干细胞中的基因异构体和AIM 4的分化。这些研究有望提供 首个生物信息学平台用于提高我们对重复基因和复杂基因异构体的理解 生物医学背景下的综合研究。

PROJECT SUMMARY / ABSTRACT Many researches have indicated the prevalence and important functions of repetitive genes and gene isoforms, especially on stem cell biology and developmental biology. While the development of the existing techniques to characterize transcriptome based on Next Generation Sequencing (NGS), has dramatically accelerated the research of different transcriptomic events and has led to many important biological findings, the abundance estimation of repetitive genes and genes isoforms remain a challenging problem. Hence, many downstream quantitative analyses, such as differential expression analysis and network construction are hindered by this limit. As the new long-read techniques have been optimized to convey robust sequencing data of transcriptome with more unambiguous alignment, it brings in new discernible information that is useful for addressing certain challenging but important transcriptomic problems. Our objective is to develop a series of bioinformatics methods to perform more reliable quantitative and function analyses of repetitive genes and gene isoforms, including abundance estimation, network construction and function prediction. Aim 1 is to identify quantification errors and the incorrectly quantified genes and gene isoforms. Aim 2 is to solve the problem of quantification by data integration. Aim 3 is to construct gene isoform network and find the possible isoform-specific functions by network analysis. The methods will be applied to study the expression and function of repetitive genes and gene isoforms in human stem cells and differentiations in Aim 4. These studies are anticipated to provide the first bioinformatics platform for improve our understanding of repetitive genes and gene isoforms with complex biomedical context in a comprehensive manner.