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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Transcription Factors02:16

Transcription Factors

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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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...
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General Transcription Factors01:30

General Transcription Factors

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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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Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Related Experiment Video

Updated: Aug 31, 2025

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.

Ren Yi1, Kyunghyun Cho1,2,3, Richard Bonneau1,2,3,4

  • 1Department of Computer Science, New York University, New York, NY 10011, USA.

Bioinformatics (Oxford, England)
|August 23, 2022
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Summary

NetTIME, a new multitask learning framework, accurately predicts cell-type-specific transcription factor binding sites at base-pair resolution. This method improves knowledge transfer for data-limited factors and cell types, outperforming existing approaches.

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Last Updated: Aug 31, 2025

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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

  • Computational biology
  • Genomics
  • Machine learning

Background:

  • Accurate prediction of cell-type-specific transcription factor (TF) binding sites is crucial for understanding gene regulation.
  • Current models face challenges due to skewed data distribution, with limited TF and cell types having abundant binding data.
  • Existing transfer learning methods often rely on shallow models, yielding low-resolution predictions.

Purpose of the Study:

  • To develop NetTIME, a novel multitask learning framework for high-resolution, cell-type-specific TF binding site prediction.
  • To enable effective knowledge transfer from data-rich to data-limited TFs and cell types.
  • To improve the accuracy and efficiency of TF binding prediction models.

Main Methods:

  • NetTIME utilizes a multitask learning approach with high-dimensional embedding vectors to capture TF and cell-type identities.
  • A linear-chain conditional random field (CRF) is employed for classifying binding predictions, removing the need for probability thresholds and reducing noise.
  • The framework enables base-pair resolution predictions.

Main Results:

  • NetTIME demonstrates superior performance compared to state-of-the-art methods (Catchitt, Leopard) in both supervised and transfer learning settings.
  • The multitask learning strategy proves more efficient than single-task approaches, leveraging increased data availability.
  • The model successfully facilitates accurate transfer predictions across diverse TFs and cell types.

Conclusions:

  • NetTIME offers a robust and efficient solution for predicting cell-type-specific TF binding sites with high resolution.
  • The framework effectively addresses data scarcity issues through multitask learning and knowledge transfer.
  • NetTIME represents a significant advancement in computational tools for genomic regulatory analysis.