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Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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CRUP: a comprehensive framework to predict condition-specific regulatory units.

Anna Ramisch1, Verena Heinrich1, Laura V Glaser1

  • 1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany.

Genome Biology
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

We developed Condition-specific Regulatory Units Prediction (CRUP) software to identify dynamic gene regulatory units from epigenetic data. This tool links enhancers to target genes, aiding in understanding gene regulation in conditions like rheumatoid arthritis.

Keywords:
3D interactionDifferential analysisEnhancer dynamicsEnhancer predictionEpigeneticsGene regulationHistone modificationRandom forest

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Area of Science:

  • Genomics and Bioinformatics
  • Epigenetics
  • Systems Biology

Background:

  • Understanding gene regulation is crucial for deciphering cellular function and disease mechanisms.
  • Epigenetic modifications, such as histone modifications, play a key role in dynamic gene regulation.
  • Identifying regulatory units, comprising enhancers and their target genes, is essential for mapping gene control networks.

Purpose of the Study:

  • To introduce Condition-specific Regulatory Units Prediction (CRUP), a novel software tool.
  • To infer condition-specific regulatory units, including dynamically changing enhancers and their target genes, from epigenetic data.
  • To provide a reliable enhancer prediction method applicable across cell types and species.

Main Methods:

  • Development of a novel pre-trained enhancer predictor using histone modification ChIP-seq data.
  • Assignment of enhancers to specific biological conditions.
  • Correlation of enhancer activity with gene expression data to derive regulatory units.
  • Application and validation of the CRUP workflow on a rheumatoid arthritis model.

Main Results:

  • The CRUP software successfully infers condition-specific regulatory units.
  • The enhancer predictor demonstrates reliability across diverse cell types and species.
  • Application to a rheumatoid arthritis model identified known disease-associated enhancer-gene pairs.
  • New candidate regulatory elements and target genes implicated in rheumatoid arthritis were discovered.

Conclusions:

  • CRUP provides a robust computational framework for predicting condition-specific regulatory units.
  • The software facilitates the discovery of novel gene regulatory mechanisms in various biological contexts.
  • CRUP aids in identifying potential therapeutic targets by revealing disease-associated regulatory elements and genes.