Related Experiment Video
Updated: Aug 10, 2026
12:24
PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
Published on: July 2, 2010
Identification of transcription factor binding sites with variable-order Bayesian networks
1Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel. bengal@eng.tau.ac.il
Bioinformatics (Oxford, England)
|March 31, 2005
Summary
We introduce variable-order Bayesian network (VOBN) models for identifying transcription factor binding sites (TFBSs). These models offer improved accuracy over traditional methods like position weight matrix (PWM) models for TFBS identification.
Area of Science:
- Computational Biology
- Bioinformatics
- Genomics
Background:
- Transcription factor binding sites (TFBSs) are crucial for gene regulation.
- Existing models like Position Weight Matrix (PWM) and Markov models have limitations in capturing complex dependencies.
- There is a need for more accurate and flexible models for TFBS identification.
Purpose of the Study:
- To propose a novel class of Variable-Order Bayesian Network (VOBN) models.
- To generalize existing models like PWM, Markov, and Bayesian networks for TFBS identification.
- To leverage context-specific dependencies between nucleotides within TFBSs.
Main Methods:
- Developed Variable-Order Bayesian Network (VOBN) models.
- Applied VOBN models to identify sigma-70 binding sites in Escherichia coli.
- Utilized a replicated stratified-holdout experiment with a fixed true-negative rate of 99.9%.
Main Results:
- VOBN models achieved higher accuracy in distinguishing TFBSs from non-promoter sequences compared to conventional models.
- A foreground VOBN model of order 1 and a background variable-order Markov (VOM) model of order 5 yielded a true-positive (TP) rate of 47.56%.
- This represents a significant improvement over the best conventional model's TP rate of 44.39%.
Conclusions:
- VOBN models offer a more flexible and accurate approach for TFBS identification.
- The context-specific modeling in VOBNs effectively captures nucleotide dependencies.
- VOBN models show significant potential for advancing genomic sequence analysis and understanding gene regulation.
More Related Videos
Related Concept Videos
02:16Transcription Factors
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...
01:49Conserved Binding Sites
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
02:16Transcription Factors
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...
02:13Cooperative 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 dimers that...
01:49Conserved Binding Sites
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
02:13Cooperative 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 dimers that...