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Transcription Factors

Transcription 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...

General Transcription Factors

General Transcription 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...

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Related Experiment Video

Updated: Nov 12, 2025

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

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

Published on: April 16, 2018

Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution.

Hongyang Li¹, Yuanfang Guan¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Genome Research

|March 20, 2021

Summary

This summary is machine-generated.

Leopard, a new deep learning method, accurately predicts transcription factor (TF) binding sites at single-nucleotide resolution. This approach significantly improves upon existing methods and offers a substantial speedup for TF binding landscape analysis.

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Related Experiment Videos

Last Updated: Nov 12, 2025

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

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

Published on: April 16, 2018

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

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

Published on: February 7, 2019

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Understanding cell type-specific transcription factor (TF) binding is vital for biological processes and diseases.
Experimental TF binding profiling is resource-intensive, limiting comprehensive analysis.
Existing computational methods lack cell-specific accuracy or high resolution.

Purpose of the Study:

To develop a novel deep learning approach for predicting TF binding sites at single-nucleotide resolution.
To improve the accuracy and efficiency of TF binding landscape prediction across diverse cell types.

Main Methods:

Developed Leopard, a deep learning model utilizing a many-to-many neural network architecture.
Evaluated Leopard's performance against state-of-the-art methods like Anchor and FactorNet.
Assessed prediction accuracy using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC).

Main Results:

Leopard achieved high predictive performance with an average AUROC of 0.982 and AUPRC of 0.208.
Significantly outperformed Anchor and FactorNet, improving AUPRC by 19% and 27% at 200-bp resolution.
Demonstrated a hundredfold to thousandfold speedup compared to existing many-to-one machine learning methods.

Conclusions:

Leopard provides accurate, high-resolution TF binding site predictions.
The method offers a significant advancement in computational TF binding analysis.
Leopard enables faster and more comprehensive exploration of TF binding landscapes.