增强子
计算生物学
基因
生物
基因调控网络
RNA序列
推论
转录因子
基因表达调控
转录组
计算机科学
基因表达
遗传学
人工智能
作者
Yang Li,Anjun Ma,Yizhong Wang,Qi Guo,Cankun Wang,Shuo Chen,Hongjun Fu,Bingqiang Liu,Qin Ma
标识
DOI:10.1101/2022.12.15.520582
摘要
ABSTRACT Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner Forest Problem model, a hybrid biclustering pipeline, and submodular optimization to infer enhancer-driven gene regulatory networks from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer relation prediction, and enhancer-gene discovery. Application of STREAM to an Alzheimer’s disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relationships associated with pseudotime, as well as key TF-enhancer-gene relationships and TF cooperation underlying tumor cells.
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