Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type–specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4. Next, we exploit DeepMEL to analyze enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement or enhancer activation. Finally, DeepMEL identifies orthologous enhancers across distantly related species, where sequence alignment fails, and the model highlights specific nucleotide substitutions that underlie enhancer turnover. DeepMEL can be used from the Kipoi database to predict and optimize candidate enhancers and to prioritize enhancer mutations. In addition, our computational strategy can be applied to other cancer or normal cell types.

中文翻译：

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使用深度学习对增强子逻辑进行跨物种分析。


破译增强子的基因组调控密码是生物学中的一个关键挑战，因为该密码是细胞身份的基础。更好地了解增强子的工作原理将改善对非编码基因组变异的解释，并有助于生成基因治疗的细胞类型特异性驱动因素。在这里，我们探索深度学习和跨物种染色质可及性分析的结合，以构建可解释的增强子模型。我们应用这种策略来破译黑色素瘤中的增强子代码，这是由于存在不同的黑色素瘤细胞状态而进行的相关案例研究。我们使用 6 个不同物种的 26 个黑色素瘤样本的染色质可及性数据训练并验证了一个名为 DeepMEL 的深度学习模型。我们展示了 DeepMEL 在 CAGI5 挑战中的预测准确性，它在IRF4黑色素瘤增强子方面显着优于现有模型。接下来，我们利用 DeepMEL 来分析增强子结构，并确定两种不同黑色素瘤状态中核心调节复合物的准确转录因子结合位点，每个转录因子在核小体置换或增强子激活方面具有不同的作用。最后，DeepMEL 识别了远缘相关物种中序列比对失败的直系同源增强子，并且该模型突出显示了增强子周转背后的特定核苷酸替换。可以使用 Kipoi 数据库中的 DeepMEL 来预测和优化候选增强子并确定增强子突变的优先级。此外，我们的计算策略可以应用于其他癌症或正常细胞类型。