Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.
Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.
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DOI：
10.1101/gr.260851.120
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发表时间：
2021-06
期刊：
影响因子：
7
通讯作者：
Aerts S
中科院分区：
文献类型：
--
作者：
Atak ZK;Taskiran II;Demeulemeester J;Flerin C;Mauduit D;Minnoye L;Hulselmans G;Christiaens V;Ghanem GE;Wouters J;Aerts S
增强子和启动子中的基因组序列变异会对细胞状态和表型产生重大影响。然而，筛选个人基因组或癌症基因组中数以百万计的候选变异，以识别那些影响顺式调节功能的变异，仍然是一个重大挑战。解释非编码基因组变异得益于可解释的人工智能，以预测和解释突变对基因调控的影响。在这里，我们为10个黑色素瘤细胞株生成了具有匹配的染色质可及性、组蛋白修饰和基因表达的阶段性完整基因组。我们发现，对黑色素瘤染色质可及性数据进行专门的深度学习模型DeepMEL2的训练可以捕获黑素细胞和间充质样黑色素瘤细胞状态的各种调节程序。该模型的性能优于基于Motif的变体评分以及更一般的深度学习模型。我们在每个黑色素瘤基因组中检测到成百上千的等位基因特异性染色质可及性变异体(ASCAVs)，其中15%-20%可以用转录因子结合位点的增减来解释。相当一部分ASCAVs是由AP-1结合的变化引起的，这一点得到了匹配的CHIP-SEQ数据的证实，以确定Jun和FOSL1的等位基因特异性结合。最后，通过用GABPA的ChIP-SEQ数据增强DeepMEL2模型，可以高度自信地识别TERT启动子突变以及额外的ETS基序增益。总之，我们提出了一种新的综合基因组学方法和深度学习模型来识别和解释染色质可及性和基因表达的等位基因失衡的功能增强子突变。
Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%–20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.
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影响因子：
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作者：
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