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Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

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...

Cooperative Binding of Transcription Regulators

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Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...

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A prior-based integrative framework for functional transcriptional regulatory network inference.

Alireza F Siahpirani¹, Sushmita Roy^2,3

¹Department of Computer Sciences, University of Wisconsin-Madison, 1210 W. Dayton St. Madison, WI 53706-1613, USA.

Nucleic Acids Research

|October 31, 2016

Summary

Inferring gene regulatory networks is challenging. Integrating gene expression with sequence motifs improves accuracy and prediction, identifying key transcription factors and enabling stress-specific network analysis.

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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

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Published on: February 11, 2019

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Transcriptional regulatory networks control gene expression.
Inferring these networks genome-wide is crucial but challenging.
Current expression-based methods lack overlap with experimental data.

Purpose of the Study:

Develop a novel algorithm for regulatory network inference.
Integrate gene expression data with auxiliary datasets.
Analyze discrepancies between inferred and experimental networks.

Main Methods:

Developed a probabilistic graphical model-based algorithm.
Integrated gene expression with sequence-specific motifs.
Analyzed various expression perturbation datasets, including natural genetic variation.

Main Results:

Networks integrating expression and motifs show higher agreement with experimental data.
These integrated networks are more predictive of gene expression than motif-only networks.
Natural genetic variation is the most informative perturbation for inference.

Conclusions:

The developed algorithm improves regulatory network inference accuracy.
Identified core transcription factors with predictable targets.
Demonstrated utility for stress-specific network inference and regulator prioritization.