ArticleBriefings in bioinformatics2024
Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies.Frontiers in neurology · 2024Pooled it
- Annotation-Based Gene-Peak Links Improve Regulatory Network Prediction of Gene Expression in Human Kidney Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.Briefings in bioinformatics · 2025Article
- Quantifying the impact of genetic mutations on enhancer dynamics.bioRxiv : the preprint server for biology · 2025Article
- ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.Genomics, proteomics & bioinformatics · 2025Article
- A large-scale benchmark for network inference from single-cell perturbation data.Communications biology · 2025Article
- A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.Patterns (New York, N.Y.) · 2024Article
- CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq.Briefings in bioinformatics · 2023Article
- Integration of Single-Cell RNA and Bulk RNA Sequencing Reveals Cellular Heterogeneity and Identifies Survival-Associated Regulatory Networks in Glioblastoma.IET systems biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) 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 eGRNs 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 linkage prediction, and enhancer-gene relation 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 relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.
Indexed as
Identifiers
What OpenQuestion holds
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.