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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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General Transcription Factors01:30

General 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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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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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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Related Experiment Video

Updated: Oct 8, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.

Hao Lu1, Luyu Ma1, Cheng Quan1

  • 1Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China.

Genomics, Proteomics & Bioinformatics
|January 1, 2022
PubMed
Summary

Identifying functional non-coding variants is crucial for understanding human genetics. RegVar, a deep neural network framework, accurately predicts the tissue-specific impact of these regulatory variants on gene expression.

Keywords:
Deep neural networkExpression quantitative trait locusExpression regulationNon-coding variantVariant prioritization

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Area of Science:

  • Genomics
  • Computational Biology
  • Human Genetics

Background:

  • Non-coding genomic variants are abundant in trait-associated variations.
  • Identifying functional non-coding variants and their target genes remains a significant challenge in human genetics.
  • A systematic method to assess regulatory variant impact on gene expression is lacking.

Purpose of the Study:

  • To introduce RegVar, a deep neural network (DNN)-based computational framework.
  • To accurately predict the tissue-specific impact of non-coding regulatory variants on target genes.
  • To provide a tool for assessing the regulatory impact of variants on putative target genes across various human tissues.

Main Methods:

  • Development of a deep neural network (DNN) computational framework named RegVar.
  • Learning genomic characteristics from massive variant-gene expression associations across diverse human tissues.
  • Benchmarking RegVar against existing non-coding variant prioritization methods.

Main Results:

  • RegVar accurately predicts the tissue-specific impact of non-coding regulatory variants on target genes.
  • The framework demonstrates superior performance compared to current methods in identifying regulatory variants.
  • RegVar robustly learns genomic features associated with gene expression across multiple human tissues.

Conclusions:

  • RegVar offers a powerful new framework for assessing the regulatory impact of non-coding variants.
  • The tool facilitates the linking of regulatory variants to their potential target genes in a tissue-specific manner.
  • RegVar is accessible as a web server for broader research application.