Gene regulatory networks control the complex programs that drive development. Deciphering the connections between transcription factors (TFs) and target genes is challenging, in part because TFs bind to thousands of places in the genome but control expression through a subset of these binding events. We hypothesize that we can combine natural variation of expression levels and predictions of TF binding sites to identify TF targets. We gather RNA-seq data from 71 genetically distinct F1 Drosophila melanogaster embryos and calculate the correlations between TF and potential target genes' expression levels, which we call “regulatory strength.” To separate direct and indirect TF targets, we hypothesize that direct TF targets will have a preponderance of binding sites in their upstream regions. Using 14 TFs active during embryogenesis, we find that 12 TFs showed a significant correlation between their binding strength and regulatory strength on downstream targets, and 10 TFs showed a significant correlation between the number of binding sites and the regulatory effect on target genes. The general roles, e.g. bicoid’s role as an activator, and the particular interactions we observed between our TFs, e.g. twist’s role as a repressor of sloppy paired and odd paired, generally coincide with the literature.

中文翻译：

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使用基因表达协变推断黑腹果蝇自然种群中的转录因子调控模式

基因调控网络控制着推动发展的复杂程序。破译转录因子 (TF) 和靶基因之间的联系具有挑战性，部分原因是 TF 与基因组中的数千个位置结合，但通过这些结合事件的一个子集控制表达。我们假设我们可以结合表达水平的自然变化和 TF 结合位点的预测来识别 TF 目标。我们从 71 个基因不同的 F1 黑腹果蝇胚胎收集 RNA-seq 数据，并计算 TF 与潜在靶基因表达水平之间的相关性，我们称之为“调节强度”。为了区分直接和间接 TF 目标，我们假设直接 TF 目标将在其上游区域具有优势结合位点。使用胚胎发生期间活跃的 14 个 TF，我们发现12个TFs的结合强度与对下游靶标的调控强度显着相关，10个TFs的结合位点数量与对靶基因的调控作用显着相关。一般作用，例如bicoid 作为激活剂的作用，以及我们观察到的TFs 之间的特定相互作用，例如扭曲作为马虎配对和奇数配对的抑制因子的作用，通常与文献一致。