Related Experiment Video
Updated: May 16, 2026
11:34
Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
Searching for transcription factor binding sites in vector spaces
1Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.
BMC Bioinformatics
|December 19, 2012
Summary
This study introduces a flexible framework for identifying transcription factor binding sites (TFBS) using vector spaces. Novel negative-to-positive vector (NPV) and optimal discriminating vector (ODV) methods automatically optimize TFBS identification for individual transcription factors.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Transcription factor binding site (TFBS) identification is crucial for understanding gene regulation.
- Existing computational methods for TFBS identification often require manual comparison to find the best approach for each transcription factor.
- There is a need for automated optimization of TFBS identification methods tailored to specific transcription factors.
Purpose of the Study:
- To develop a flexible computational framework for TFBS identification.
- To introduce novel methods that enable automatic optimization for individual transcription factors.
- To improve the accuracy and efficiency of TFBS prediction.
Main Methods:
- Proposed a novel framework for TFBS identification in vector spaces.
- Introduced the negative-to-positive vector (NPV) and optimal discriminating vector (ODV) methods for constructing query vectors.
- Developed k-NPV and k-ODV methods to leverage motif subtype identification.
Main Results:
- The proposed NPV and ODV methods significantly outperformed existing state-of-the-art methods like ungapped likelihood and position-specific scoring matrices.
- The framework successfully identified motif subtypes for transcription factors, enhancing the performance of k-NPV and k-ODV methods.
- Independent validation on ChIP-seq data confirmed the superior performance of ODV and NPV methods in TFBS identification.
Conclusions:
- The developed framework offers high flexibility for TFBS identification.
- The novel NPV and ODV methods enable automatic identification of transcription factor-specific subspaces.
- The source code for the TFBS search methods is publicly available for research use.
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...
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...
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...
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...