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

基本信息

批准号：
10929710
负责人：
Kin Fai Au
金额：
$ 68.52万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10929710
关键词：
Acceleration 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 Methodology Methods Modeling Names Nature Pathway Analysis Pattern Pilot Projects Prevalence Primitive foregut structure 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 Transcript Zebrafish 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 cells tool transcriptome transcriptome sequencing transcriptomics

项目摘要

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.

项目概要/摘要许多研究表明重复基因和基因亚型的普遍性和重要功能，尤其是干细胞生物学和发育生物学。在发展现有技术的同时基于下一代测序 (NGS) 表征转录组，极大地加速了对不同转录组事件的研究并产生了许多重要的生物学发现，重复基因和基因亚型的估计仍然是一个具有挑战性的问题。因此，许多下游这阻碍了定量分析，例如差异表达分析和网络构建限制。由于新的长读长技术已经过优化，可以传递可靠的转录组测序数据通过更明确的对齐，它带来了新的可识别信息，有助于解决某些问题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对重复基因和基因亚型进行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 是确定量化错误以及错误量化的基因和基因亚型。目标2是解决量化问题通过数据整合。目标3是构建基因异构体网络并找到可能的异构体特异性功能通过网络分析。该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亚型和目标 4 中的分化。这些研究预计将提供第一个生物信息学平台，用于提高我们对复杂的重复基因和基因亚型的理解全面地了解生物医学背景。