ArticlePLoS computational biology2022
Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- RNA stability: a novel perspective on gene regulatory networks in bovine mastitis.BMC genomics · 2025Article
- RVINN: a flexible modeling for inferring dynamic transcriptional and post-transcriptional regulation using physics-informed neural networks.Bioinformatics (Oxford, England) · 2025Article
- Dissecting and improving gene regulatory network inference using single-cell transcriptome data.Genome research · 2023Article
- Cell Type-Specific Whole-Genome Landscape of ΔFOSB Binding in the Nucleus Accumbens After Chronic Cocaine Exposure.Biological psychiatry · 2023Article
- An Integrative Multiomics Framework for Identification of Therapeutic Targets in Pulmonary Fibrosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.