Genetics of dark matter transcription in yeast

酵母中暗物质转录的遗传学

• 批准号: 7807961
• 负责人: Rachel Beth Brem
• 金额: $ 29.06万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 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/7807961
• 关键词: Affect; Applications Grants; Be++ element; 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

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. PUBLIC HEALTH RELEVANCE: The treatment of human disease with small molecule drugs requires laborious testing of compounds for specificity and potency, and is largely restricted to the targeting of proteins with small ligand binding sites. Synthetic oligonucleotide therapeutics that interact with RNAs are emerging as a potentially revolutionary alternative, but the field of RNA-based drugs is still in its infancy. This proposal aims to develop approaches and tools that infer function of novel RNAs on a high-throughput scale, ultimately widening the landscape of targets for therapeutics.

描述（由申请人提供）：基因座编码未知功能元件的转录在人类基因组和许多模型系统中广泛存在。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。这种对功能性rna的探索部分是由于rna作为人类疾病治疗靶点的潜力，以及作为治疗药物本身的潜力。研究者建议开发方法，以酵母为模型，在高通量规模上推断未注释转录本的功能。此前，该研究人员率先对基因不同的个体之间mRNA表达差异进行了遗传分析。在实验基因组学、软件开发和分子遗传学的基础上，研究者现在建议开发一种针对酵母中未注释的、假定的非编码rna的相关策略。主要目标是利用已知基因和未注释转录本的共同调控来推断后者的功能。该项目将绘制导致rna水平变化的酵母菌株之间的DNA差异——包括带注释的和未带注释的。遗传作图软件将识别主调控因子的多态性，每一个调控因子都会影响trans中多个下游目标的表达。在这样一个规则中，功能基因组分析将在已知基因中发现共同的通路成员，从而推断未注释的转录本也在同一途径中起作用。绘图软件还将识别顺式调控元件的多态性，每一个都影响附近编码的转录物的水平；这将允许发现新的rna的启动子和其他顺式调控区域。分子方法将为预测的单个rna的功能和调控提供实验证实。这些rna的发现，以及支持它们的软件工具，将成为未来后生动物研究的跳板。