Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type–specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type–specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type–specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

中文翻译：

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用于推断细胞类型特异性转录网络的动态调节模块网络

转录调控网络的变化可以显着改变细胞命运。为了深入了解转录动力学，一些研究在发育过程的不同阶段通过平行的转录组学和表观基因组学测量来分析大量的多组学数据集。然而，整合这些数据以推断特定于细胞类型的调控网络是一项重大挑战。我们提出了动态调节模块网络 (DRMN)，这是一种推断细胞类型特定的顺式调节网络及其动力学的新方法。DRMN 整合表达、染色质状态和可及性以预测顺式- 上下文特定表达的调节器，其中上下文可以是细胞类型、发育阶段或时间点，并使用多任务学习来捕获跨线性和层次相关上下文的网络动态。我们应用 DRMN 来研究三个发育过程中的调控网络动力学，每个过程都显示出不同的时间关系并测量调控基因组数据集的不同组合：细胞重编程、肝脏去分化和正向分化。DRMN 确定了驱动细胞类型特异性表达模式的已知和新型监管机构，表明其广泛适用于从线性和分层相关的多组学数据集中检查基因监管网络的动态。