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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Regulation of Bacterial Virulence01:28

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Pathogenic bacteria employ a range of regulatory mechanisms to modulate the expression of virulence genes in response to environmental and host-derived signals. These mechanisms ensure that virulence factors are expressed only under favorable conditions, thereby optimizing infection and survival strategies.Mechanisms of Virulence RegulationKey regulatory strategies include:Two-Component Systems: These consist of a membrane-bound sensor kinase and a cytoplasmic response regulator. Environmental...
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Transcriptional Regulation: Riboswitches01:23

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Riboswitches are RNA elements that regulate gene expression by altering their secondary structures in response to specific effector molecules. These elements, located in the leader regions of certain mRNAs, act as transcriptional regulators by toggling between alternative conformations to control downstream gene expression. Riboswitch-mediated regulation is a precise mechanism for modulating biosynthetic pathways, as exemplified by the riboflavin biosynthesis pathway in Bacillus...
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Related Experiment Video

Updated: Apr 6, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Running spell-check to identify regulatory variants.

Martin Kircher1, Jay Shendure1

  • 1Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

Nature Genetics
|July 30, 2015
PubMed
Summary

Identifying non-coding genetic variants impacting gene expression and disease risk is challenging. A new method accurately predicts variant effects by modeling cell type-specific regulatory elements.

Area of Science:

  • Human Genetics
  • Genomics
  • Molecular Biology

Background:

  • Pinpointing the functional impact of non-coding genetic variants on gene expression and disease risk remains a significant challenge in human genetics.
  • Non-coding regions of the genome are often difficult to interpret, yet harbor crucial regulatory elements.

Purpose of the Study:

  • To develop and validate a broadly applicable computational approach for predicting the effects of non-coding genetic variants.
  • To improve the accuracy and interpretability of variant effect predictions compared to existing tools.

Main Methods:

  • The study introduces a novel approach that explicitly models cell type-specific regulatory motifs.
  • This method integrates genomic data to predict how genetic variants influence gene regulation.

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Main Results:

  • The developed approach demonstrates enhanced accuracy in predicting variant effects on gene expression.
  • Predictions generated by this method are more interpretable, offering insights into the mechanisms of variant impact.

Conclusions:

  • The new computational approach provides a powerful tool for understanding the role of non-coding variants in human disease.
  • This method advances the field of functional genomics by offering more precise and understandable variant effect predictions.