Transcription factor recognition models with modified nucleobases

转录因子驱动哺乳动物基因表达程序，通过在特定序列和表观基因组环境中结合DNA来启动转录。转录因子的序列特异性已经被广泛研究了几十年。表观基因组背景的研究直到后来才随着翻译后组蛋白修饰和DNA甲基化的大规模数据而成为可能。在甲基化DNA中，胞嘧啶（C）转化为修饰形式5-甲基胞嘧啶（5 mC）。DNA甲基化可以通过非序列特异性5 mC结合蛋白影响转录，这可以沉默基因。尽管如此，一些转录因子优先结合含有5 mC的特定序列，而不是未修饰的C，反之亦然。随着时间的推移，甚至在细胞分裂期间，细胞保持稳定的DNA甲基化状态。这意味着甲基化对转录因子结合的影响比其他更短暂的表观基因组标记更持久。除了5 mC，最近的研究揭示了其他三种胞嘧啶修饰的生物学重要性，5-羟甲基胞嘧啶（5 hmC），5-甲酰基胞嘧啶（5 fC）和5-羧基胞嘧啶（5caC）。其他研究表明，这些修饰也会影响转录因子对DNA的识别，就像5 mC一样。然而，没有工具来预测转录因子对考虑修饰的核碱基的存在或不存在的结合位点的亲和力。这阻碍了对胞嘧啶修饰对基因调控的影响的充分理解。 为了消除这一障碍，了解基因调控，我们将开发和验证计算模型和方法，以确定转录因子结合亲和力的背景下，表观遗传核碱基修饰。我们将通过以下具体目标实现这一目标： 目标1。我们将创建一个算法，使用多个证据来源来确定可能的DNA修饰状态。 目标2.我们将建立一个新的数学模型，转录因子结合偏好的修饰和未修饰的核苷酸序列。 目标3：我们将开发一种混合方法，用于基序发现，该方法使用体内染色质免疫沉淀测序（ChIP-seq）数据将体外转录因子结合模型扩展到更广泛的字母表。 申请人Michael霍夫曼博士接受过机器学习、生物信息学和生物化学方面的正式培训。他发表了多篇关于转录因子结合位点预测和表观遗传状态解释的论文。他负责管理多个国家的研究用户每天使用的可持续软件的开发。 这项研究计划将导致更好地了解核碱基修饰和基因调控之间的关系。它还将提供软件，基因组注释和模型文件，使基因组学社区能够使用我们的方法和结果。

Transcription factors drive mammalian gene expression programs, initiating transcription by binding DNA in specific sequence and epigenomic contexts. Sequence specificity of transcription factors has been studied extensively for decades. The study of epigenomic contexts became possible only later with large-scale data on post-translational histone modifications and DNA methylation. In methylated DNA, cytosine (C) undergoes conversion to a modified form, 5-methylcytosine (5mC). DNA methylation can influence transcription through non-sequence-specific 5mC-binding proteins which can silence genes. Nonetheless, some transcription factors preferentially bind specific sequences containing 5mC rather than unmodified C, or vice versa. The cell maintains stable DNA methylation states over time and even across cell divisions. This means methylation can exhibit a more sustained impact on transcription factor binding than other, more transient, epigenomic marks. In addition to 5mC, recent studies reveal the biological importance of three other cytosine modifications, 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC). Other studies show that these modifications also affect transcription factor recognition of DNA just as 5mC does. There are no tools, however, to predict transcription factor affinity for binding sites that consider the presence or absence of modified nucleobases. This prevents a full understanding of the impact of cytosine modifications on gene regulation. To remove this barrier to the understanding of gene regulation, we will develop and validate computational models and methods to determine transcription factor binding affinities in the context of epigenetic nucleobase modifications. We will accomplish this through the following specific aims: Aim 1. We will create an algorithm to determine likely DNA modification state using multiple evidence sources. Aim 2. We will construct a new mathematical model of transcription factor binding preference for sequences of modified and unmodified nucleotides. Aim 3. We will develop a hybrid approach for motif discovery that expands in vitro transcription factor binding models to the broader alphabet using in vivo chromatin immunoprecipitation-sequencing (ChIP-seq) data. The applicant, Dr. Michael Hoffman, has formal training in machine learning, bioinformatics, and biochemistry. He has published multiple papers on transcription factor binding site prediction and epigenetic state interpretation. He has managed the development of sustainable software used by research users in multiple countries daily. This research program will lead to a better understanding of the relationship between nucleobase modifications and gene regulation. It will also deliver software, genome annotations, and model files to enable the genomics community to use our methods and results.