In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide folding as assayed by chromosome conformation capture methods. Still, how a given DNA sequence encodes a particular locus-specific folding pattern remains unknown. Here we present a convolutional neural network, Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of an orientation-specific grammar for CTCF binding sites. Akita learns predictive nucleotide-level features of genome folding, revealing effects of nucleotides beyond the core CTCF motif. Once trained, Akita enables rapid in silico predictions. Accounting for this, we demonstrate how Akita can be used to perform in silico saturation mutagenesis, interpret eQTLs, make predictions for structural variants and probe species-specific genome folding. Collectively, these results enable decoding genome function from sequence through structure.

在间期，人类基因组序列在三个维度上折叠成丰富多样的基因座特异性接触模式。Cohesin 和 CTCF（CCCTC 结合因子）是关键的调节剂；如通过染色体构象捕获方法测定的那样，扰乱任一的水平极大地破坏了全基因组折叠。尽管如此，给定的 DNA 序列如何编码特定位点特定的折叠模式仍然未知。在这里，我们提出了一个卷积神经网络 Akita，它可以仅从 DNA 序列准确预测基因组折叠。Akita 学习的表示法强调了 CTCF 结合位点特定方向语法的重要性。Akita 学习了基因组折叠的预测性核苷酸级特征，揭示了核心 CTCF 基序之外核苷酸的影响。经过训练后，Akita 可以快速进行计算机预测。考虑到这一点，我们展示了 Akita 如何用于执行计算机饱和诱变、解释 eQTL、预测结构变异和探测物种特异性基因组折叠。总的来说，这些结果能够从序列到结构解码基因组功能。