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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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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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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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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq
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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq

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Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.

Mehran Karimzadeh1,2,3, Michael M Hoffman4,5,6,7

  • 1Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.

Genome Biology
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

Predicting transcription factor (TF) binding sites is challenging as most sites lack sequence motifs. Virtual ChIP-seq integrates multiple data types to accurately predict TF binding, outperforming sequence-based methods.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Traditional methods for predicting transcription factor (TF) binding sites rely on sequence similarity to known motifs.
  • However, a significant portion of actual TF binding sites do not conform to these known sequence preferences, and many TFs exhibit non-sequence-specific binding.
  • This limitation hinders accurate prediction of TF binding events across different cellular contexts.

Purpose of the Study:

  • To develop a novel computational method, Virtual ChIP-seq, for predicting individual TF binding in new cell types.
  • To integrate diverse biological data, including gene expression, TF binding data from other cell types, and chromatin accessibility, to improve prediction accuracy.
  • To overcome the limitations of sequence-motif-based prediction methods.

Main Methods:

  • The Virtual ChIP-seq approach was developed to predict TF binding by integrating multiple data sources.
  • It incorporates learned associations between gene expression and TF binding patterns.
  • The method also leverages existing TF binding site data from different cell types and chromatin accessibility data specific to the target cell type.

Main Results:

  • Virtual ChIP-seq demonstrates superior performance compared to methods relying solely on sequence preference.
  • The approach successfully predicted binding for 36 transcription factors with a Matthews Correlation Coefficient (MCC) greater than 0.3.
  • This indicates a significant improvement in identifying functional TF binding sites.

Conclusions:

  • Virtual ChIP-seq offers a more robust and accurate method for predicting transcription factor binding sites, particularly in scenarios where sequence motifs are not conserved.
  • The integration of multi-modal data significantly enhances the predictive power for TF binding across various cell types.
  • This advancement has implications for understanding gene regulation and cellular function in diverse biological contexts.