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多组织个人表观基因组和变异影响模型的EN-TEx资源

Joel Rozowsky¹, Jiahao Gao², Beatrice Borsari³

¹Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.

|March 31, 2023

概括

在二倍体基因组中,EN-TEx资源将基因变异映射到异位基因特异性活性. 这使得基因表达的准确预测,并将变体与个性化功能基因组学的疾病关联联系起来.

科学领域:

基因组学
分子生物学
生物信息学

背景情况:

功能性基因组学旨在将遗传变异与分子表型联系起来.
目前的方法仅限于使用单一的单 haploid 基因组.
对于个性化医疗来说,了解特定的基因调节至关重要.

研究的目的:

介绍EN-TEx资源,这是一个功能性基因组学数据的全面集合.
开发用于分析双倍基因组中的等位基因特异性活性的方法.
探索遗传变异对分子表型和疾病关联的影响.

主要方法:

来自四个捐赠者的各种组织和检测的1,635个开放式数据集的生成.
使用长读相和结构变异识别将数据集映射到匹配的双倍基因组中.
开发一个深度学习的转换器模型,从序列上下文预测基因特异性活动.

主要成果:

创建一个超过100万个具有协调性单基因活动的单基因特定位点的目录.
对影响等位基因特异性基因表达的变异敏感的序列动机的识别.
在基因特异位点,GWAS位点和eQTL之间显示强烈的关联.
开发用于将eQTL信息传输到缺少配置的组织的模型.

结论:

EN-TEx为研究双倍基因组中的等位基因特异性基因调节提供了宝贵的资源.
基于序列上下文的预测模型可以准确地捕获等位基特定活动.
该资源有助于将功能基因组学数据与遗传关联研究整合起来,以更好地了解疾病.
能更准确地进行个性化功能基因组学和疾病风险预测.

关键词:

编码美国基因基因特异性活动电子质量标准功能表观基因组功能性基因组学基因组注释个人基因组预测模型结构变体组织特异性变压器模型

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