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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.

项目总结/摘要许多研究表明重复基因和基因亚型的普遍存在及其重要功能，尤其是干细胞生物学和发育生物学。虽然现有技术的发展，基于下一代测序（NGS）的转录组特征，大大加速了研究不同的转录组事件，并导致了许多重要的生物学发现，丰富的重复基因和基因同种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，如差异表达分析和网络构建极限由于新的长读段技术已被优化以传达转录组的稳健测序数据，通过更明确的对齐，它带来了新的可辨别的信息，这些信息对于解决某些具有挑战性但重要的转录组学问题。我们的目标是发展一系列的生物信息学对重复基因和基因同种型进行更可靠的定量和功能分析的方法，包括丰度估算、网络构建和功能预测。目标1是确定量化错误和不正确定量的基因和基因亚型。目标二是解决量化问题通过数据集成。目的3：构建基因异构体网络，寻找可能的异构体特异性功能网络分析。该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种型和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，用于提高我们对重复基因和复杂基因亚型的理解以全面的方式进行生物医学研究。