Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin.
Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin.
复制标题
通过预测开放染色质的组织特异性差异推断哺乳动物组织特异性调节保护。
DOI：
10.1186/s12864-022-08450-7
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发表时间：
2022-04-11
期刊：
影响因子：
4.4
通讯作者：
Pfenning AR
中科院分区：
文献类型：
--
作者：
Kaplow IM;Schäffer DE;Wirthlin ME;Lawler AJ;Brown AR;Kleyman M;Pfenning AR
进化保守是推断基因组功能重要性的宝贵工具，包括许多物种中至关重要的区域和那些经历了趋同进化的区域。测试序列保守性的计算方法主要是检查一个或多个核苷酸在大进化距离上比对的能力的算法。虽然这些基于核苷酸序列的方法已被证明对蛋白质编码基因和一些非编码元件是强大的，但它们未能捕获许多增强子的保守性，这些增强子是控制基因表达的空间和时间模式的远端调控元件。增强子的功能由复杂的，通常是组织和细胞类型特异性的密码控制，该密码将转录因子结合位点和其他调控相关序列模式的组合与调控活性联系起来。因此，即使核苷酸周转率很高，邻位互补增强子区域的功能也可以在大的进化距离上保持不变。我们提出了一种新的基于机器学习的方法来评估增强子的保守性，该方法利用增强子活性的组合序列代码，而不是依赖于单个核苷酸的比对。我们首先训练一个卷积神经网络模型，该模型可以预测哺乳动物中组织特异性开放染色质，这是增强子活性的代理。接下来，我们应用该模型来区分基因组序列预测该组织中保守功能与调控活性丧失的情况。我们提出了系统地评估模型性能的标准，并使用它们来证明我们的模型准确地预测灵长类动物和啮齿动物物种之间开放染色质的组织特异性保守和分歧，大大优于领先的基于核苷酸序列的方法。然后，我们应用我们的模型来预测数百种哺乳动物的大脑和肝脏开放染色质区域的直系同源物的开放染色质，并发现与神经元活动相关的大脑增强子比一般人群具有更强的预测谱系特异性开放染色质的倾向。这里提出的框架提供了一种机制，注释组织特异性的调控功能，在数百个基因组和研究增强子进化预测的监管差异，而不是核苷酸水平的保守测量。在线版本包含补充材料，可通过10.1186/s12864-022-08450-7获得。
Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high. We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin. The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements. The online version contains supplementary material available at 10.1186/s12864-022-08450-7.
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影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
4.5
作者：
Daane JM;Auvinet J;Stoebenau A;Yergeau D;Harris MP;Detrich HW 3rd
通讯作者：
Detrich HW 3rd
影响因子：
64.8
作者：
Armstrong J;Hickey G;Diekhans M;Fiddes IT;Novak AM;Deran A;Fang Q;Xie D;Feng S;Stiller J;Genereux D;Johnson J;Marinescu VD;Alföldi J;Harris RS;Lindblad-Toh K;Haussler D;Karlsson E;Jarvis ED;Zhang G;Paten B
通讯作者：
Paten B
影响因子：
16.8
作者：
Berthelot C;Villar D;Horvath JE;Odom DT;Flicek P
通讯作者：
Flicek P
影响因子：
4.5
作者：
Albert FW;Somel M;Carneiro M;Aximu-Petri A;Halbwax M;Thalmann O;Blanco-Aguiar JA;Plyusnina IZ;Trut L;Villafuerte R;Ferrand N;Kaiser S;Jensen P;Pääbo S
通讯作者：
Pääbo S