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

使用位点特异性功能分类重新注释组蛋白修饰的机器学习方法

基本信息

  • 批准号:
    MR/T022620/1
  • 负责人:
  • 金额:
    $ 163.91万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Fellowship
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    未结题

项目摘要

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.
人体含有大约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。为了履行它们的适当角色,它们都执行不同的遗传程序,同时包含完整基因组指令(DNA)的相同副本,该副本从单个亲本细胞传递下来。在专门的细胞中,大多数程序都被关闭,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易于执行。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子的情况下,胚胎干细胞不能分化。在各种疾病中也观察到了表观遗传功能障碍:例如,如果正常情况下沉默的程序被激活,细胞可能会改变它们的身份;例如,当它们的表观遗传机制出现故障时,白色血细胞可能会变成癌细胞。表观基因组包括许多化学改变,它们存在于DNA序列本身的“顶部”。例如,在某些DNA序列特征出现时,可以将甲基添加到DNA中以沉默相应的遗传元件。此外,DNA序列包裹在组蛋白周围,形成“串珠”类型的结构。通过化学修饰单个组蛋白,相邻的“珠子”可以彼此紧密接触,从而形成密集且难以接近的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。组蛋白修饰是由一组不同的酶动态建立的,所谓的“表观遗传编辑器”。它们也可以被一些特定的“表观基因组擦除器”主动清除。由此建立的表观基因组模式被“表观遗传学读取器”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,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,转录活性受到表观基因组修饰的调节。然而,尽管我们对表观遗传机制的理解有了深入的研究和实质性的进展,组蛋白密码仍然是个谜。测量表观基因组快照的技术进步导致了可用数据的爆炸。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准确理解。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。沿着我的合作研究者,我建议在复杂的计算预测和湿实验室实验之间循环,以提供表观基因组模式的动态概况。特别是,我们计划通过快速贬低个别作家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行动。我还将使用统计方法来分析动态表观基因组和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,以了解表观遗传对健康发育和疾病的贡献。此外,该项目的部分内容将在Cyber Valley Campus Tuebingen进行,该校园拥有因果机器学习技术的一些世界领导者。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles
  • DOI:
    10.1101/2022.07.30.502157
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jacob Schreiber;C. Boix;Jin-Wook Lee;Hongyang Li;Yuanfang Guan;Chun-Chieh Chang;Jen-Chien Chang;Alex Hawkins-Hooker;Bernhard Schölkopf;Gabriele Schweikert;Mateo Rojas Carulla;Arif Canakoglu;Francesco Guzzo;Luca Nanni;M. Masseroli;Mark James Carman;Pietro Pinoli;Chenyang Hong;Kevin Y. Yip;J. P. Spence;S. S. Batra-S.;Yun S. Song;Shaun Mahony;Zheng Zhang;Wuwei Tan;Yang Shen;Yuanfei Sun;Minyi Shi;Jessika Adrian;R. Sandstrom;Nina P. Farrell;J. Halow;Kristen Lee;Lixia Jiang;Xinqiong Yang;Charles Epstein;J. Strattan;Michael Snyder;M. Kellis;W. S. Noble;A. Kundaje
  • 通讯作者:
    Jacob Schreiber;C. Boix;Jin-Wook Lee;Hongyang Li;Yuanfang Guan;Chun-Chieh Chang;Jen-Chien Chang;Alex Hawkins-Hooker;Bernhard Schölkopf;Gabriele Schweikert;Mateo Rojas Carulla;Arif Canakoglu;Francesco Guzzo;Luca Nanni;M. Masseroli;Mark James Carman;Pietro Pinoli;Chenyang Hong;Kevin Y. Yip;J. P. Spence;S. S. Batra-S.;Yun S. Song;Shaun Mahony;Zheng Zhang;Wuwei Tan;Yang Shen;Yuanfei Sun;Minyi Shi;Jessika Adrian;R. Sandstrom;Nina P. Farrell;J. Halow;Kristen Lee;Lixia Jiang;Xinqiong Yang;Charles Epstein;J. Strattan;Michael Snyder;M. Kellis;W. S. Noble;A. Kundaje
Getting Personal with Epigenetics: Towards Machine-Learning-Assisted Precision Epigenomics
  • DOI:
    10.1101/2022.02.11.479115
  • 发表时间:
    2022-02
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alex Hawkins-Hooker;G. Visonà;Tanmayee Narendra;Mateo Rojas-Carulla;B. Scholkopf;G. Schweikert
  • 通讯作者:
    Alex Hawkins-Hooker;G. Visonà;Tanmayee Narendra;Mateo Rojas-Carulla;B. Scholkopf;G. Schweikert
A historical perspective of biomedical explainable AI research.
  • DOI:
    10.1016/j.patter.2023.100830
  • 发表时间:
    2023-09-08
  • 期刊:
  • 影响因子:
    6.5
  • 作者:
    Malinverno, Luca;Barros, Vesna;Ghisoni, Francesco;Visona, Giovanni;Kern, Roman;Nickel, Philip J.;Ventura, Barbara Elvira;Simic, Ilija;Stryeck, Sarah;Manni, Francesca;Ferri, Cesar;Jean-Quartier, Claire;Genga, Laura;Schweikert, Gabriele;Lovri, Mario;Rosen-Zvi, Michal
  • 通讯作者:
    Rosen-Zvi, Michal
Acute depletion of the ARID1A subunit of SWI/SNF complexes reveals distinct pathways for activation and repression of transcription.
  • DOI:
    10.1016/j.celrep.2021.109943
  • 发表时间:
    2021-11-02
  • 期刊:
  • 影响因子:
    8.8
  • 作者:
    Blümli S;Wiechens N;Wu MY;Singh V;Gierlinski M;Schweikert G;Gilbert N;Naughton C;Sundaramoorthy R;Varghese J;Gourlay R;Soares R;Clark D;Owen-Hughes T
  • 通讯作者:
    Owen-Hughes T
Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning.
  • DOI:
    10.1038/s41467-023-40211-2
  • 发表时间:
    2023-08-07
  • 期刊:
  • 影响因子:
    16.6
  • 作者:
    Hawkins-Hooker, Alex;Visona, Giovanni;Narendra, Tanmayee;Rojas-Carulla, Mateo;Schoelkopf, Bernhard;Schweikert, Gabriele
  • 通讯作者:
    Schweikert, Gabriele
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Gabriele Schweikert其他文献

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles
  • DOI:
    10.1186/s13059-025-03494-w
  • 发表时间:
    2025-02-13
  • 期刊:
  • 影响因子:
    9.400
  • 作者:
    Jacob Matthew Schreiber;Carles A. Boix;Jin wook Lee;Hongyang Li;Yuanfang Guan;Chun-Chieh Chang;Jen-Chien Chang;Alex Hawkins-Hooker;Bernhard Schölkopf;Gabriele Schweikert;Mateo Rojas Carulla;Arif Canakoglu;Francesco Guzzo;Luca Nanni;Marco Masseroli;Mark James Carman;Pietro Pinoli;Chenyang Hong;Kevin Y. Yip;Jefrey P. Spence;Sanjit Singh Batra;Yun S. Song;Shaun Mahony;Zheng Zhang;Wuwei Tan;Yang Shen;Yuanfei Sun;Minyi Shi;Jessika Adrian;Richard S. Sandstrom;Nina P. Farrell;Jessica M. Halow;Kristen Lee;Lixia Jiang;Xinqiong Yang;Charles B. Epstein;J. Seth Strattan;Bradley E. Bernstein;Michael P. Snyder;Manolis Kellis;William S. Noble;Anshul Bharat Kundaje
  • 通讯作者:
    Anshul Bharat Kundaje

Gabriele Schweikert的其他文献

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