基因调控对于细胞功能和稳态至关重要。它涉及控制特定基因产物的产生并促成基因表达中的组织特异性变异的多种机制。基因失调导致疾病,强调需要了解这些机制。计算方法已经联合研究了转录因子(TFs),microRNA(miRNA),和信使RNA(mRNA)来研究基因调控网络。然而,在理解基因调控网络方面仍然存在知识空白。另一方面,在最近的实验研究中,超增强子(SE)与miRNA的生物发生和功能有关,除了它们在细胞身份和疾病进展中的关键作用。然而,利用SE在破译基因调控网络中的潜力的统计/计算方法仍然明显缺乏。然而,为了了解miRNA对mRNA的影响,现有的统计/计算方法可以更新,或者可以通过考虑模型中的SE来开发新的方法。在这次审查中,我们将利用TF和miRNA数据来理解基因调控网络的现有计算方法分为三个大领域,并探讨整合增强子/SE的挑战.这三个领域包括瓦解间接监管网络,识别网络图案,并通过解剖基因调控因子进行富集途径鉴定。我们假设解决这些挑战将增强我们对基因调控的理解,帮助识别治疗靶标和疾病生物标志物。我们认为,构建统计/计算模型来剖析SE在预测miRNA对基因调控的影响中的作用对于应对这些挑战至关重要。
Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this
review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.