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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
General Transcription Factors01:30

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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Related Experiment Video

Updated: May 14, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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Published on: March 12, 2021

BARTsc identifies key transcriptional regulators from single-cell omics data.

Hongpan Zhang1,2, Lei Kang1, Jingyi Wang1

  • 1Department of Genome Sciences, University of Virginia, Charlottesville, VA 22908, USA.

Biorxiv : the Preprint Server for Biology
|May 13, 2026
PubMed
Summary

BARTsc accurately predicts functional transcriptional regulators from single-cell omics data by integrating ChIP-seq profiles. This method outperforms existing tools, identifying key regulators in diverse systems like pancreatic cancer.

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Last Updated: May 14, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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Published on: January 10, 2019

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

  • Single-cell biology
  • Functional genomics
  • Computational biology

Background:

  • Inferring transcriptional regulatory mechanisms from single-cell omics data is crucial but challenging.
  • Existing methods using co-expression or motif enrichment have limitations.

Purpose of the Study:

  • To present BARTsc, a novel computational method for accurate prediction of functional transcriptional regulators (TRs) from single-cell omics data.
  • To leverage public ChIP-seq profiles to infer cis-regulatory profiles and identify associated TRs.

Main Methods:

  • BARTsc infers cis-regulatory profiles from differential genomic features in unimodal (scRNA-seq, scATAC-seq) or bimodal (scMultiome) data.
  • It identifies TRs by associating their binding profiles with inferred cis-regulatory profiles.
  • TR activity is quantified across cell clusters, predicting key regulators for each.

Main Results:

  • BARTsc successfully identifies active TRs and cell-type-defining regulators across diverse systems (mouse cortex, PBMCs, PDAC).
  • It consistently outperforms state-of-the-art methods when benchmarked against AI-assisted, literature-supported regulator collections.
  • BARTsc identified NEFLA as a novel key regulator in PDAC, with experimental validation of its role in tumor proliferation.

Conclusions:

  • BARTsc is a robust and versatile computational method for predicting functional TRs from single-cell omics data.
  • It provides deeper insights into cell-type-specific regulatory programs.
  • Facilitates the discovery of key regulators across diverse biological systems.