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Genetics of dark matter transcription in yeast

酵母中暗物质转录的遗传学

基本信息

批准号：
8462634
负责人：
Rachel Beth Brem
金额：
$ 27.77万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-01 至 2014-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8462634
关键词：
Affect Applications Grants Beryllium Binding Sites Bioinformatics Biological Models Biology Chromosome Mapping Cis Tests Collection Computer software DNA DNA mapping Data Eukaryota Functional RNA Future Genes Genetic Genetic Polymorphism Genetic Transcription Genome Genomics Genotype Goals Human Genome Individual Lead Ligand Binding Maps Measures Methods Modeling Molecular Molecular Genetics Nucleic Acid Regulatory Sequences Oligonucleotides Organism Pathway interactions Pharmaceutical Preparations Population Proteins Publishing RNA Reading Regulation Regulatory Element Regulon Research Personnel Saccharomycetales Sampling Software Tools Specificity Technology Testing Therapeutic Therapeutic Agents Transcript Untranslated RNA Variant Work Yeast Model System Yeasts base functional genomics genetic analysis genome-wide human disease improved infancy innovation mRNA Expression meetings member novel novel strategies promoter public health relevance small molecule software development statistics therapeutic target tool

项目摘要

DESCRIPTION (provided by applicant): Transcription from loci encoding no known functional elements is widespread in the human genome, and in many model systems. A key challenge in genome biology is to determine which such "dark matter" transcripts are functionally relevant. This search for functional RNAs is motivated in part by the potential of RNAs as targets for treatment of human disease, and as therapeutic agents themselves. The investigator proposes to develop methods to infer function of un-annotated transcripts on a high-throughput scale, using yeast as a model. Previously, the investigator pioneered the genetic analysis of mRNA expression differences between genetically diverse individuals. On the basis of this demonstrated expertise with experimental genomics, software development, and molecular genetics, the investigator now proposes to develop a related strategy for un-annotated, putative noncoding RNAs in yeast. The principal goal is to harness the co-regulation of known genes and un-annotated transcripts to infer function of the latter. The project will map DNA differences between yeast strains that cause variation in levels of RNAs-both annotated and un-annotated. Software for genetic mapping will identify polymorphisms in master regulators, each of which affects the expression of multiple downstream targets in trans. In such a regulon, functional genomic analysis will find common pathway membership among known genes, leading to the inference that un- annotated transcripts also function in the same pathway. Mapping software will also identify polymorphisms in cis-regulatory elements, each of which affects levels of a transcript encoded nearby; this will allow the discovery of promoters and other cis-acting regulatory regions for novel RNAs. Molecular methods will provide experimental confirmation of the predicted function and regulation of individual RNAs. Discoveries of these RNAs, and the software tools that enable them, will serve as a springboard for future work in metazoans.

描述(申请人提供)：从编码未知功能元件的基因座转录广泛存在于人类基因组和许多模型系统中。基因组生物学的一个关键挑战是确定哪些“暗物质”转录本在功能上是相关的。这种寻找功能性RNA的部分动机是RNA作为治疗人类疾病的靶点以及作为治疗剂本身的潜力。这位研究人员建议开发一种方法，以酵母为模型，在高通量范围内推断未注释的转录本的功能。此前，这位研究人员率先对遗传差异的个体之间的mRNA表达差异进行了遗传分析。基于实验基因组学、软件开发和分子遗传学方面的专业知识，研究人员现在建议为酵母中未注释的、假定的非编码RNA开发一种相关的策略。主要目标是利用已知基因和未注释转录本的共同调节来推断后者的功能。该项目将绘制引起RNA水平变化的酵母菌株之间的DNA差异--包括注释和未注释的。用于基因作图的软件将识别主调控子中的多态，每个多态都会影响反式转录中多个下游靶标的表达。在这样的调节子中，功能基因组分析将在已知基因之间找到共同的途径成员关系，从而得出结论，未注释的转录本也在相同的途径中发挥作用。测绘软件还将识别顺式调控元件中的多态，每个多态都会影响附近编码的转录本的水平；这将允许发现启动子和其他顺式作用的调控区域，用于新的RNA。分子方法将为预测的单个RNA的功能和调节提供实验证实。这些RNA的发现以及使它们成为可能的软件工具，将成为后生动物未来工作的跳板。