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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
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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...
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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...
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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...
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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...
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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Benchmarking tools for transcription factor prioritization.

Leonor Schubert Santana1, Alejandro Reyes1, Sebastian Hoersch1

  • 1Novartis Biomedical Research, Basel, Switzerland.

Computational and Structural Biotechnology Journal
|May 31, 2024
PubMed
Summary

Identifying transcription factors (TFs) driving gene expression is crucial for drug discovery. This study evaluated TF prediction tools using chromatin profiling data, nominating three frontrunners: RcisTarget, MEIRLOP, and monaLisa.

Keywords:
ATAC-seqBenchmarkChIP-seqChromatinH3K27acMotif enrichmentPWMRegulatory regionsTranscription factor

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Area of Science:

  • Genomics and molecular biology
  • Epigenetics and gene regulation
  • Computational biology and bioinformatics

Background:

  • Gene expression is spatiotemporally regulated by transcription factor (TF) binding to regulatory elements.
  • Histone acetylation (H3K27ac) marks active regulatory regions, detectable via ChIP-seq.
  • Predictive tools for TF binding to DNA sequences exist, but their performance in real-world scenarios needs evaluation.

Purpose of the Study:

  • To evaluate the performance of existing TF prediction tools.
  • To identify TF s driving gene expression programs for potential drug target identification.
  • To nominate superior TF prioritization tools based on chromatin profiling data.

Main Methods:

  • Curated 84 H3K27ac ChIP-seq experiments with TF perturbations (knockout/overexpression).
  • Applied nine published TF prediction tools to these datasets.
  • Assessed tool performance in identifying perturbed TFs.

Main Results:

  • Nominated RcisTarget, MEIRLOP, and monaLisa as frontrunner TF prioritization tools.
  • Identified commonalities and areas for improvement among the evaluated tools.
  • Demonstrated the utility of real-world chromatin profiling data for tool benchmarking.

Conclusions:

  • RcisTarget, MEIRLOP, and monaLisa show promise for TF prioritization in gene regulation studies.
  • The study provides insights for developing improved TF binding prediction tools.
  • Accurate TF identification is critical for understanding disease mechanisms and developing targeted therapies.