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

多能性调控和分化中重复基因和基因亚型的定量和功能分析平台

• 批准号: 10451490
• 负责人: Kin Fai Au
• 金额: $ 60万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10451490
• 关键词: Address; Adopted; Alternative Splicing; Anterior; Bioinformatics; Biological; Biology; Biomedical Research; Complex; Computer software; Data; Development; Developmental Biology; Differentiated Gene; Embryonic Development; Endoderm; Event; Exons; Foundations; Genes; Genetic; Human; Laboratory Research; Lead; Methodology; Methods; Modeling; Names; Nature; Pathway Analysis; Pattern; Pilot Projects; Prevalence; Primitive foregut structure; Problem Solving; Protein Isoforms; Publications; Publishing; Regulation; Repetitive Sequence; Research; Retrotransposon; Role; Sampling; Series; Solid; Spliced Genes; Statistical Models; Structure; Structure of primordial sex cell; Techniques; Technology; The Jackson Laboratory; Time; Transcript; Zebrafish; base; cost; dark matter; data integration; differential expression; experimental study; gene network; human embryonic stem cell; human stem cells; improved; indexing; innovation; next generation sequencing; novel; pluripotency; pluripotency factor; precision medicine; public health relevance; rapid technique; statistics; stem cell biology; stem cell differentiation; stem cells; tool; transcriptome; transcriptome sequencing; transcriptomics

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.

项目摘要 /摘要 许多研究表明重复基因和基因同工型的流行率和重要功能， 特别是关于干细胞生物学和发育生物学。而现有技术的发展 根据下一代测序（NGS）表征转录组，已经显着加速了 对不同转录组事件的研究，导致许多重要的生物学发现，丰度 重复基因和基因同工型的估计仍然是一个具有挑战性的问题。因此，许多下游 定量分析（例如差异表达分析和网络构建）受到了阻碍 限制。随着新的长阅读技术已被优化，以传达转录组的强大测序数据 有了更明确的一致性，它带来了新的可识别信息 具有挑战性但重要的转录组问题。我们的目标是开发一系列生物信息学 进行重复基因和基因同工型的更可靠的定量和功能分析的方法， 包括丰度估计，网络构建和功能预测。目标1是确定量化 误差以及错误量化的基因和基因同工型。目标2是解决量化问题 通过数据集成。 AIM 3是构建基因同工型网络并找到可能的同工型特异性功能 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， 人类干细胞中的基因同工型和AIM 4中的分化。预计这些研究将提供 第一个生物信息学平台，以提高我们对复杂基因和基因同工型的理解 生物医学的环境以全面的方式。