The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with orthogonal experimental data, providing insights into generalization but offering limited insights into their decision-making process. Existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we present cis-regulatory element model explanations (CREME), an in silico perturbation toolkit that interprets the rules of gene regulation learned by a genomic DNN. Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions. CREME can provide interpretations across multiple scales of genomic organization, from cis-regulatory elements to fine-mapped functional sequence elements within them, offering high-resolution insights into the regulatory architecture of the genome. CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation.

用于预测基因表达的大规模、基于序列的深度神经网络（DNN）的兴起给其评估和解释带来了挑战。目前的评估将 DNN 预测与正交实验数据结合起来，提供了对泛化的见解，但对其决策过程的见解有限。现有的模型可解释性工具主要集中于主题分析，当解释较长的序列时，该分析会变得复杂。在这里，我们提出了顺式调控元件模型解释 (CREME)，这是一种计算机微扰工具包，可以解释基因组 DNN 学习的基因调控规则。将 CREME 应用于最先进的 DNN Enformer，我们确定了增强或沉默基因表达的顺式调控元件，并表征了它们复杂的相互作用。 CREME 可以提供跨多个基因组组织尺度的解释，从顺式调控元件到其中精细映射的功能序列元件，提供对基因组调控架构的高分辨率见解。 CREME 提供了一个强大的工具包，用于将基因组 DNN 的预测转化为基因调控的机制见解。