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Related Concept Videos

Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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.
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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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DNA-affinity-purified Chip (DAP-chip) Method to Determine Gene Targets for Bacterial Two component Regulatory Systems
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TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.

Chao Cheng1, Renqiang Min, Mark Gerstein

  • 1Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.

Bioinformatics (Oxford, England)
|November 1, 2011
PubMed
Summary

A new probabilistic model, Target Identification from Profiles (TIP), quantitatively measures transcription factor (TF) regulatory relationships. TIP improves upon simple methods by considering TF binding profiles for more accurate target gene identification.

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Published on: March 7, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • ChIP-seq and ChIP-chip are standard for identifying transcription factor (TF) binding sites and target genes.
  • Conventional target gene identification relies on simple proximity (e.g., within 2 kb of TSS), neglecting binding site distribution and TF-specific distances.
  • Existing methods fail to capture the nuances of TF binding patterns around transcription start sites (TSS).

Purpose of the Study:

  • To develop a quantitative probabilistic model for measuring regulatory relationships between TFs and target genes.
  • To introduce a novel approach that overcomes limitations of conventional TF target gene identification methods.
  • To provide a more accurate and robust method for analyzing TF binding data.

Main Methods:

  • Proposed a probabilistic model named Target Identification from Profiles (TIP).
  • TIP builds an averaged TF binding profile around the TSS.
  • Uses this profile to weight TF binding sites, generating a continuous regulatory score for TF-gene relationships.

Main Results:

  • TIP provides a quantitative 'regulatory' score linking TFs to potential target genes.
  • The score can be converted into ranked lists of target genes with significance estimates.
  • TIP demonstrates advantages over simple methods, validated by motif occurrence and knock-out experiments.

Conclusions:

  • TIP offers a more robust and quantitative approach to TF target gene identification.
  • The model is less sensitive to experimental variations like sequencing depth and peak-calling methods.
  • TIP enables more cost-effective utilization of ChIP-seq data.