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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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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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Cis-regulatory Sequences02:02

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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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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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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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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Updated: Jun 17, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants.

Simon C Biddie1,2, Giovanna Weykopf3, Elizabeth F Hird4

  • 1MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK. Simon.Biddie@ed.ac.uk.

Genome Biology
|August 6, 2024
PubMed
Summary

Identifying functional genetic variants from genome-wide association studies (GWAS) is challenging. We developed FINDER, a framework using DNase footprints and enhancer RNA (eRNA) to prioritize functional single nucleotide variants (SNVs) for complex traits.

Keywords:
Functional geneticsFunctional genomicsGenome-wide association studyNon-coding genomeNon-coding variantsSingle nucleotide polymorphismSingle nucleotide variants

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Area of Science:

  • Genomics
  • Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify numerous genetic variants linked to complex traits and diseases.
  • Variants are often in non-coding regions, making functional identification difficult.
  • Current methods for prioritizing functional variants using regulatory element markers are insufficient.

Purpose of the Study:

  • To systematically analyze markers of active regulatory elements for their ability to identify functional variants.
  • To develop a robust framework for prioritizing functional single nucleotide variants (SNVs) from GWAS data.

Main Methods:

  • Benchmarking against molecular quantitative trait loci (molQTL) data from various assays.
  • Analyzing DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility.
  • Developing the FINDER (Functional SNV IdeNtification using DNase footprints and eRNA) framework.

Main Results:

  • The combination of DNase footprints and divergent enhancer RNA (eRNA) effectively identifies functional variants with high precision.
  • This signature significantly reduces candidate variant sets for functional validation.
  • The FINDER framework provides a novel approach for prioritizing functional SNVs.

Conclusions:

  • The FINDER framework demonstrates utility in prioritizing variants for complex traits like leukocyte count.
  • It aids in predicting functional variants in diseases such as asthma.
  • Findings support the development of predictive scoring algorithms and functionally informed fine-mapping approaches for GWAS.