Evolutionary Modeling/Prediction of ncRNA Genes in Flies

概率进化模型的使用是比较基因组学的一个组成部分。应用 包括设计进化基因发现软件;筛选编码DNA序列， 选择;鉴定基因组中的调控元件;改进同源物的检测;以及 预测有害的SNP。这种进化模型仍然不发达，特别是对于 仅作为非编码RNA（ncRNA）表达的基因亚类。然而，越来越多的生物医学 对这类基因的兴趣：现在怀疑microRNA调节广泛的靶点， 被病毒用来沉默宿主的转录;小的干扰RNA正在被探索作为治疗 治疗;存在特异性靶向RNA结构（例如细菌核糖体或逆转录病毒结合）的药物 位点，如HIV的Rev反应元件）; RNA基序被称为“核糖开关”，在遗传上与- 工程化的“适体”在其区别性结合小分子配体的能力方面，最近已经被 在原核生物中发现;许多催化ncRNA（“核酶”）在人类和细菌细胞中具有活性;以及 许多上述技术（例如核糖开关/适体/核酶）开始被开发， 合成生物学家，例如作为具有巨大生物医学潜力的新型报告构建体。 在这里，我们建议为一个集中的、生物学上可测试的案例研究开发ncRNA进化模型： 通过对12个果蝇基因组的比较分析来鉴定ncRNA基因。在计算上，我们 我将用这个例子来推动我们正在进行的RNA分析和进化方法的发展。 建模，调整我们成功的“xrate”和“stemloc”程序，用于evolutionarystochastic 上下文无关文法在实验上，我们将通过湿实验室验证方法来测试我们的预测，例如 RT-PCR和测序，在我们的合作者和当地可用资源，如 伯克利果蝇基因组计划的cDNA文库。我们所有的软件都将在网上免费提供。

The use of probabilistic evolutionary models is an integral part of comparative genomics. Applications include design of evolutionary genefinding software; screening of coding DNA sequences for sites under selection; identification of regulatory elements in genomes; improved detection of homologues;and prediction of deleterious SNPs. Such evolutionary models remain under-developed, particularly for the subclass of genes expressed solely as noncoding RNA (ncRNA). However, there is increasing biomedical interest in this class of genes: microRNAs are now suspected to regulate a wide range of targets, and are used by viruses to silence host transcription; small interfering RNAs are being explored as atherapeutic treatment; drugs exist that specifically target RNA structure (e.g. bacterial ribosomes, or retroviral binding sites such as HIV's Rev Response Element); RNA motifs known as "riboswitches", echoing genetically- engineered "aptamers" in their ability to discriminatively bind small-molecule ligands, have recently been discovered in prokaryotes; many catalytic ncRNAs ("ribozymes") are active in human and bacterial cells; and many of the above technologies (e.g. riboswitches/ aptamers/ ribozymes) are beginning to be exploited by synthetic biologists e.g. as novel reporter constructs, with great biomedical potential. Here, we propose to develop ncRNA evolutionary models for a focussed, biologically testable case study: the identification of ncRNA genes by comparative analysis of twelve fruitfly genomes. Computationally, we will use this example to drive forward our ongoing methods development in RNA analysis and evolutionary modeling, adapting our successful "xrate" and "stemloc" programs for use with evolutionarystochastic context-free grammars. Experimentally, we will test our predictions by wet-lab validation methods such as RT-PCR and sequencing, with the help of our collaboratorsand locally available resources such as the Berkeley Drosophila Genome Project's cDNA libraries. All our software will be freely available online.