Machine Learning Methods to Re-annotate Histone Modifications with Locus-specific Functional Classification

人体包含大约200种不同的细胞类型，例如神经细胞或血细胞，每种细胞都有其特定的外观和功能。为了履行其适当的角色，它们都执行不同的遗传程序集，同时包含从单个亲本细胞遗传下来的完整基因组指令(DNA)的相同副本。在专门的单元中，大多数程序被关闭，使它们能够高效地专注于给定的任务。这就是表观遗传机制的作用：它们包装和组织DNA，使某些片段被屏蔽并沉默，而其他部分可访问并随时可执行。因此，表观遗传机制对正常发育和健康至关重要。例如，在缺乏某些表观遗传因素的情况下，胚胎干细胞无法分化。表观遗传功能障碍也在各种疾病中观察到：例如，如果正常沉默的程序被激活，细胞可能会改变它们的身份；例如，当白细胞的表观遗传机制出现故障时，它们可能会变成癌细胞。表观基因组包括一些化学变化，这些变化存在于DNA序列本身的“顶部”。例如，在出现某些DNA序列特征时，可以在DNA中添加甲基，以使相应的遗传元件沉默。此外，DNA序列包裹在组蛋白蛋白周围，形成了一种“串珠”类型的结构。通过对单个组蛋白进行化学修饰，相邻的“珠子”可以彼此紧密接触，从而形成密集的、难以接触的DNA区域。或者，一组不同的组蛋白修饰可以导致开放和可访问的DNA结构域。组蛋白修饰是由一大组不同的酶动态建立的，即所谓的“表观遗传编写者”。它们也可以被一些特定的“表观基因组擦除器”主动去除。这样建立起来的表观基因组模式被“表观遗传读者”所认可。有趣的是，一些稳定状态的表观基因组修饰与转录活性有很好的相关性，这表明效应蛋白确实提供了表观基因组模式的读出。这些发现导致了组蛋白密码假说，根据该假说，转录活性由表观基因组修饰来调节。然而，尽管我们在理解表观遗传机制方面进行了密集的研究并取得了实质性进展，但组蛋白密码仍然是个谜。表观基因组快照测量技术的进步导致了可用数据的爆炸性增长。然而，由于这些标记的高度复杂性和不断变化的性质，对它们的含义和读数缺乏准确的理解。今天，我看到了一个独特的机会，可以在复杂的机器学习技术的帮助下应对这一挑战：这些方法使用计算机系统从大数据集中‘学习’隐藏的关系。我将建立新的计算工具来捕捉支撑表观基因组标记动态变化的分子机制。与我的合作者一起，我建议在复杂的计算预测和提供表观基因组模式动态图谱的湿实验室实验之间循环。特别是，我们计划通过迅速降低个别作家的身份来扰乱表观遗传机制，以观察他们的行动如何协调其他作家和读者的操作。我还将使用统计学方法分析动态表观基因组和不断变化的基因表达之间的时空相关性。这个项目将受益于邓迪大学现有的表观遗传学专业知识，我们的努力将反过来为正在进行的项目提供信息，以了解表观遗传学对健康发展和疾病的贡献。此外，该项目的部分工作将在网络谷校区Tuebingen进行，那里聚集了一些因果机器学习技术的世界领先者。

The human body contains about 200 different cell types, e.g. nerve or blood cells, each with their specific appearances and functions. To carry out their proper roles, they all execute different sets of genetic programs while containing an identical copy of the complete genomic instructions (the DNA), which is passed down from a single parent cell. In specialized cells,the majority of programs are switched off, allowing them to efficiently focus on a given task. This is what epigenetic mechanisms do: They package and organize the DNA, such that certain bits are shielded away and silenced, while other parts are accessible and readily executable.As such, epigenetic mechanisms are vital for normal development and health. For instance, in the absence of certain epigenetic factors embryonic stem cells fail to differentiate. Epigenetic malfunctioning has also been observed in various diseases: For example, if normally silenced programs become activated, cells may change their identity; white blood cells, for instance, can turn into cancerous cells when their epigenetic machinery is faulty.The epigenome comprises a number of chemical alterations, which exist 'on top' of the DNA sequence itself. For example, at the occurrence of certain DNA sequence features, methyl groups can be added to the DNA to silence corresponding genetic elements. Additionally, the DNA sequence is wrapped around histone proteins forming a "beads-on-a-string" type of architecture. By chemically modifying individual histone proteins, neighboring 'beads' can be brought into tight contact with each other thus forming dense and inaccessible regions of DNA. Alternatively, a different set of Histone modifications can result in open and accessible DNA domains.Histone modifications are dynamically established by a large set of different enzymes, so called 'epigenetic writers'. They can also be actively removed by a number of specific 'epigenomic erasers'. The thus established epigenomic patterns are recognized by 'epigenetic readers'. Interestingly, some steady-state epigenomic modifications are remarkably well correlated with transcriptional activity, suggesting that effector proteins are indeed providing a read-out of epigenomic patterns. These findings have lead to the histone code hypothesis, according to which transcriptional activity is regulated by epigenomic modifications. However, despite intense research and substantial progress in our understanding of epigenetic mechanisms, the histone code has remained enigmatic.Technological advances in the measurement of epigenomic snapshots have led to an explosion of available data. Yet owing to the high complexity and changing nature of these marks, a precise understanding of their meaning and readout is lacking. Today, I see a unique opportunity to tackle this challenge with the help of sophisticated machine learning technologies: These methods use computer systems to 'learn' hidden relationships from large data sets. I will build new computational tools to capture the molecular mechanisms underpinning the dynamic changes of epigenomic marks. Along with my co-investigator, I suggest cycling between sophisticated computational predictions and wet lab experiments that provide dynamic profiles of epigenomic patterns. In particular we plan to disturb the epigenetic machinery by rapidly degrading individual writers to observe how their action orchestrates operations of other writers and readers. I will also use statistical methods to analyse the spatiotemporal correlation between dynamic epigenomes and changing gene expression. This project will benefit from the existing epigenomic expertise at Dundee University and our efforts will in turn inform on-going projects to understand epigenetic contributions to healthy development and disease. In addition, parts of the project will be carried out at the Cyber Valley Campus Tuebingen, which hosts some of the world leaders in causal machine learning techniques.