Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

了解控制基因表达的遗传调控密码是分子生物学的一个重要挑战。然而，基因调控结构的各个编码区和非编码区如何相互作用并影响 mRNA 表达水平仍不清楚。在这里，我们对 20,000 多个 mRNA 数据集应用深度学习，以检查从细菌到人类等 7 种模型生物体中控制 mRNA 丰度的遗传调控密码。在所有生物体中，我们可以直接从 DNA 序列预测 mRNA 丰度，高达 82% 的转录水平变异由基因调控结构编码。通过在基因调控结构中寻找 DNA 调控基序，我们发现基序相互作用可以解释 mRNA 水平的整个动态范围。编码区和非编码区的共同进化表明，定义基因表达水平的不是单个基序或区域，而是整个基因调控结构和调控元件的特定组合。