Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.
PLoS Comput Biol. 2022 Jan 10;18(1):e1009762. doi: 10.1371/journal.pcbi.1009762. eCollection 2022 Jan.
Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.
转录因子 (TFs) 的活性是时间调节的，以调节包括发育、稳态和疾病在内的动态细胞过程。生物信息学工具的最新发展使得能够使用转录组数据分析 TF 活性。然而，由于这些方法通常使用基于外显子的靶标表达水平，因此估计的 TF 活性的时间准确性有限。为了解决这个问题，我们提出了一种基于时间序列 RNA-seq 数据中内含子水平信息的 TF 活性度量方法，并将其实现用于解码动态过程中 TF 活性的时间控制。我们表明，与基于外显子的度量相比，从内含子读取推断出的 TF 活性可以更好地再现瞬时 TF 活性。通过分析公共和我们自己的时间序列转录组数据，我们发现基于内含子的 TF 活性改善了对生物钟节律期间循环 TF 的时间相位的描述，并有助于在 T 细胞激活期间发现两个时间相反的 TF 模块。总的来说，我们预计所提出的方法将广泛适用于解码动态过程中的全局转录架构。
