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Related Concept Videos

Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
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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...
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Related Experiment Video

Updated: Jun 14, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility

Jens Uwe Loers1,2,3, Vanessa Vermeirssen1,2,3

  • 1Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium.

Briefings in Bioinformatics
|August 29, 2024
PubMed
Summary

Gene regulatory network inference is advancing with multi-omics data. Integrating transcriptomics and epigenomics at the single-cell level is key for understanding gene regulation in development and disease.

Keywords:
ATAC-seqRNA-seqgene regulatory networksmulti-omicssingle-cell

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Genomics

Background:

  • Eukaryotic gene regulation is complex, involving combinatorial, dynamic, and quantitative processes crucial for development and disease.
  • Gene regulatory networks (GRNs) model these processes, with recent advances driven by multi-omics data, especially at the single-cell level.
  • Enhancer GRNs (eGRNs) specifically model interactions between transcription factors, regulatory elements, and target genes.

Purpose of the Study:

  • To review key components for successful GRN and eGRN inference using transcriptomics and chromatin accessibility data.
  • To highlight state-of-the-art methods, challenges, and future directions in the field.
  • To emphasize the integration of multi-omics data for mechanistic network inference.

Main Methods:

  • Utilizing single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data.
  • Applying computational strategies for omics pairing, transcription factor binding site detection, and chromatin interaction analysis.
  • Exploring linear and 3D approaches for identifying regulatory element interactions.

Main Results:

  • Combinations of transcriptomics and chromatin accessibility enable fine-grained regulatory program prediction beyond simple expression correlation.
  • eGRNs provide a detailed model of molecular interactions within regulatory pathways.
  • The review covers preprocessing, metacell generation, and omics integration techniques.

Conclusions:

  • The integration of transcriptomics and epigenomics data at the single-cell level represents the new standard for mechanistic network inference.
  • Future advancements lie in integrating additional omics layers, spatiotemporal data, and shifting towards quantitative and causal modeling.
  • This approach is vital for a deeper understanding of gene regulation in biological processes and diseases.