Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transcription Factors02:16

Transcription Factors

75.5K
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...
75.5K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.3K
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...
6.3K
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

11.0K
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...
11.0K
General Transcription Factors01:30

General Transcription Factors

5.1K
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...
5.1K
Master Transcription Regulators02:23

Master Transcription Regulators

6.8K
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...
6.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Oxidative stress-driven transcriptomic remodeling in human astrocytes reveals network signatures associated with neurodegenerative and cardiovascular processes.

Computational and structural biotechnology journal·2026
Same author

Continuum architecture dynamics of vesicle tethering in exocytosis.

Cell·2026
Same author

Nrm1 is a bistable switch connecting cell cycle progression to transcriptional control.

EMBO reports·2025
Same author

Predicting cellular adaptation proteins dependent on eIF2α regulation under stress conditions: Physiological and pathophysiological implications in neuronal function.

Computational and structural biotechnology journal·2025
Same author

The C-terminal domain of SEC-10 is fundamental for exocyst function, apical organization, and cell morphogenesis in <i>Neurospora crassa</i>.

Molecular biology of the cell·2025
Same author

Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike-ACE2 Interaction.

Molecules (Basel, Switzerland)·2025
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 23, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

8.7K

SNPeBoT: a tool for predicting transcription factor allele specific binding.

Patrick Gohl1, Baldo Oliva2

  • 1Department of Medicine and Life Sciences, SBI-GRIB, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain.

BMC Bioinformatics
|March 11, 2025
PubMed
Summary
This summary is machine-generated.

We developed SNPeBoT, a deep learning model that accurately predicts how Single Nucleotide Polymorphisms (SNPs) affect transcription factor binding. This tool improves the discovery of disease-causing non-coding mutations.

Keywords:
Gene regulationNeural networkTranscription factor

More Related Videos

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
12:29

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Published on: April 16, 2018

9.2K
Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

6.6K

Related Experiment Videos

Last Updated: May 23, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

8.7K
Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
12:29

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Published on: April 16, 2018

9.2K
Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

6.6K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mutations in non-coding DNA can disrupt transcription factor binding, potentially leading to disease.
  • Understanding transcription factor binding patterns and the impact of genetic variations is crucial but limited.
  • Deep learning offers a promising approach to model these complex interactions.

Purpose of the Study:

  • To develop a deep learning model for predicting the impact of Single Nucleotide Polymorphisms (SNPs) on transcription factor binding.
  • To improve the accuracy and recall of predicting allele-specific binding (ASB) events.
  • To provide a tool for identifying non-coding mutations associated with diseases.

Main Methods:

  • Trained a Convolutional Neural Network (CNN) using allele-specific binding (ASB) data from ChIP-seq and DNA binding domain data from Protein Binding Microarrays (PBM).
  • Derived E-score profiles for reference and alternate DNA sequences associated with ASB events.
  • Utilized 18,211 E-score profiles from 113 transcription factors, split into training, validation, and testing datasets.

Main Results:

  • The developed CNN model demonstrated higher accuracy and ASB recall compared to existing benchmark prediction platforms.
  • The model effectively predicts whether SNPs lead to a gain, loss, or no change in transcription factor binding.
  • Performance was validated against established tools for SNP effect prediction.

Conclusions:

  • Introduced SNPeBoT (Single Nucleotide Polymorphism effect on Binding of Transcription Factors), available as a standalone tool and web server.
  • The enhanced prediction accuracy and recovery of ASB events can aid in discovering disease-relevant non-coding mutations.
  • SNPeBoT offers a valuable resource for genetic research and disease association studies.