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

人体包含大约200种不同类型的细胞，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。为了发挥其应有的作用，它们都执行不同的遗传程序，同时包含一个完整的基因组指令（DNA）的相同副本，这是从单个亲本细胞遗传下来的。在专门的细胞中，大多数程序被关闭，使它们能够有效地专注于给定的任务。这就是表观遗传机制所做的：它们包装和组织DNA，使某些部分被屏蔽和沉默，而其他部分则可以访问并随时执行。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，胚胎干细胞不能分化。在各种疾病中也观察到表观遗传功能失常：例如，如果通常沉默的程序被激活，细胞可能会改变其身份；例如，当白细胞的表观遗传机制出现故障时，它就会变成癌细胞。表观基因组包含许多化学变化，这些变化存在于DNA序列本身的“顶部”。例如，在某些DNA序列特征出现时，可以在DNA中加入甲基以沉默相应的遗传元件。此外，DNA序列被包裹在组蛋白上，形成一种“串珠”的结构。通过化学修饰单个组蛋白，相邻的“珠子”可以彼此紧密接触，从而形成致密且难以接近的DNA区域。另外，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。组蛋白修饰是由一组不同的酶动态建立的，被称为“表观遗传作者”。它们也可以被一些特定的“表观基因组擦除剂”主动去除。由此建立的表观基因组模式被“表观遗传解读器”识别。有趣的是，一些稳态表观基因组修饰与转录活性显著相关，这表明效应蛋白确实提供了表观基因组模式的解读。这些发现导致组蛋白编码假说，根据该假说，转录活性是由表观基因组修饰调节的。然而，尽管我们对表观遗传机制的理解进行了大量的研究和实质性的进展，组蛋白密码仍然是一个谜。测量表观基因组快照的技术进步导致了可用数据的爆炸式增长。然而，由于这些标记的高度复杂性和不断变化的性质，缺乏对其含义和读数的精确理解。今天，我看到了一个独特的机会，借助复杂的机器学习技术来解决这一挑战：这些方法使用计算机系统从大型数据集中“学习”隐藏的关系。我将建立新的计算工具来捕捉支持表观基因组标记动态变化的分子机制。我和我的合作研究者建议在复杂的计算预测和湿实验室实验之间循环，这些实验提供了表观基因组模式的动态概况。特别是，我们计划扰乱表观遗传机制，通过快速降低单个作家，观察他们的行为如何协调其他作家和读者的操作。我还将使用统计方法来分析动态表观基因组与变化的基因表达之间的时空相关性。该项目将受益于邓迪大学现有的表观基因组学专业知识，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育和疾病的贡献。此外，该项目的部分工作将在图宾根网络谷校园进行，该校园拥有一些因果机器学习技术的世界领导者。

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.