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Related Concept Videos

Epigenetic Regulation01:46

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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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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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.
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Integrative analysis of epigenetics data identifies gene-specific regulatory elements.

Florian Schmidt1,2,3,4, Alexander Marx1,2,3,5, Nina Baumgarten6,7

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Summary
This summary is machine-generated.

StitchIt accurately identifies gene regulatory elements by analyzing epigenetic signals across samples, revealing previously uncharted regulatory regions and their target genes. This method improves upon traditional approaches for understanding gene expression regulation.

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Area of Science:

  • Genomics
  • Epigenetics
  • Computational Biology

Background:

  • Epigenetic variation in non-coding regions influences gene expression.
  • Associating regulatory regions with target genes is challenging, especially for complex epigenomic signals and enhancers.
  • Existing methods struggle with heterogeneous datasets and accurately pinpointing regulatory elements.

Purpose of the Study:

  • To develop a novel computational approach, StitchIt, for dissecting epigenetic variation in a gene-specific manner.
  • To enable the detection of regulatory elements (REMs) and their target genes simultaneously without relying on sample-specific peak calls.
  • To improve the accuracy and refinement of REM detection compared to standard methods.

Main Methods:

  • StitchIt segments epigenetic signal tracks across multiple samples.
  • It simultaneously determines the location and target genes of regulatory elements.
  • The approach is designed to handle complex and heterogeneous epigenomic datasets.

Main Results:

  • StitchIt achieves more accurate and refined REM detection than standard methods, even on challenging datasets.
  • Predicted REMs show high enrichment in experimentally validated chromatin interactions and expression quantitative trait loci.
  • CRISPR-Cas9 experiments validated newly predicted REMs, confirming StitchIt's reliability.
  • StitchIt identifies thousands of putative REMs, including within superenhancers, missed by peak-based approaches.

Conclusions:

  • StitchIt offers a robust and accurate method for identifying gene regulatory elements and their targets.
  • The approach expands our understanding of the genome's regulatory landscape, suggesting a significant portion of the regulome remains undiscovered.
  • StitchIt provides a powerful tool for dissecting gene regulation and discovering novel regulatory elements.