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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.

项目摘要/摘要