A plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein–protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 datasets of human cell types and lines and demonstrate that it can accurately infer GRNs, thereby outperforming state-of-the-art methods.

中文翻译：

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多组学数据集成揭示了控制细胞类型身份的核心转录调控网络。

已经提出了大量的计算方法来从基因表达数据重建基因调控网络 (GRN)。然而，基因调控过程往往过于复杂，无法仅从转录组进行预测。在这里，我们提出了一种计算方法 Moni，它系统地整合了表观遗传学、转录组学和蛋白质 - 蛋白质相互作用，以重建核心转录因子及其控制细胞身份的辅助因子之间的 GRN。我们将 Moni 应用于 57 个人类细胞类型和细胞系的数据集，并证明它可以准确推断 GRN，从而优于最先进的方法。