ArticleNature communications2025
Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unleashing innovative cross-organ fibrosis therapies by harnessing the omics revolution.JCI insight · 2026Review
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- Dynamic transitioning between MAPK-driven and WNT-driven cell states drives intestinal cancer and shapes therapy response.Nature genetics · 2026Article
- GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer.International journal of molecular sciences · 2026Article
- Multi-omics evaluation of cell lines as models for metastatic prostate cancer.Communications biology · 2026Article
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Authors and funding
32 authors.
Funding
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Abstract
Transcription factors (TFs) and transcriptional coregulators are emerging therapeutic targets. Gene regulatory networks (GRNs) can evaluate pharmacological agents and identify drivers of disease, but methods that rely solely on gene expression often neglect post-transcriptional modulation of TFs. We present Epiregulon, a method that constructs GRNs from single-cell ATAC-seq and RNA-seq data for accurate prediction of TF activity. This is achieved by considering the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. ChIP-seq data allows motif-agonistic activity inference of transcriptional coregulators or TF harboring neomorphic mutations. Epiregulon accurately predicted the effects of AR inhibition across different drug modalities including an AR antagonist and an AR degrader, delineated the mechanisms of a SMARCA4 degrader by identifying context-dependent interaction partners, and prioritized drivers of lineage reprogramming and tumorigenesis. By mapping gene regulation across various cellular contexts, Epiregulon can accelerate the discovery of therapeutics targeting transcriptional regulators.
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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.