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Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome

机译:果蝇基因组中转录因子结合位点的核苷酸相互依赖性。

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摘要

A long-standing objective in modern biology is to characterize the molecular components that drive the development of an organism. At the heart of eukaryotic development lies gene regulation. On the molecular level, much of the research in this field has focused on the binding of transcription factors (TFs) to regulatory regions in the genome known as cis-regulatory modules (CRMs). However, relatively little is known about the sequence-specific binding preferences of many TFs, especially with respect to the possible interdependencies between the nucleotides that make up binding sites. A particular limitation of many existing algorithms that aim to predict binding site sequences is that they do not allow for dependencies between nonadjacent nucleotides. In this study, we use a recently developed computational algorithm, MARZ, to compare binding site sequences using 32 distinct models in a systematic and unbiased approach to explore nucleotide dependencies within binding sites for 15 distinct TFs known to be critical to Drosophila development. Our results indicate that many of these proteins have varying levels of nucleotide interdependencies within their DNA recognition sequences, and that, in some cases, models that account for these dependencies greatly outperform traditional models that are used to predict binding sites. We also directly compare the ability of different models to identify the known KRUPPEL TF binding sites in CRMs and demonstrate that a more complex model that accounts for nucleotide interdependencies performs better when compared with simple models. This ability to identify TFs with critical nucleotide interdependencies in their binding sites will lead to a deeper understanding of how these molecular characteristics contribute to the architecture of CRMs and the precise regulation of transcription during organismal development.
机译:现代生物学的一个长期目标是表征驱动生物发展的分子成分。基因调控是真核生物发展的核心。在分子水平上,该领域的许多研究都集中在转录因子(TF)与基因组中称为顺式调控模块(CRM)的调控区的结合上。然而,关于许多TF的序列特异性结合偏好的了解相对较少,特别是关于构成结合位点的核苷酸之间可能的相互依赖性。许多现有的旨在预测结合位点序列的算法的一个特殊限制是它们不允许不相邻核苷酸之间的依赖性。在这项研究中,我们使用一种最新开发的计算算法MARZ,以系统且无偏见的方式使用32个不同的模型比较结合位点序列,以探索对果蝇发展至关重要的15个不同TF的结合位点内的核苷酸依赖性。我们的结果表明,这些蛋白质中的许多蛋白质在其DNA识别序列中具有不同水平的核苷酸相互依赖性,并且在某些情况下,解释这些依赖性的模型大大优于用于预测结合位点的传统模型。我们还直接比较了不同模型识别CRM中已知的KRUPPEL TF结合位点的能力,并证明与简单模型相比,解释核苷酸相互依赖性的更复杂的模型表现更好。鉴定在其结合位点具有关键核苷酸相互依赖性的TF的能力将导致对这些分子特性如何促进CRM的结构以及在生物发育过程中精确调控转录的更深入了解。

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