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Related Concept Videos

General Transcription Factors01:30

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...
Transcription Factors02:16

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...
Transcription Factors02:16

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...
Transcription Initiation01:47

Transcription Initiation

Initiation is the first step of transcription in eukaryotes. Prokaryotic RNA Polymerase (RNAP) can bind to the template DNA and start transcribing. On the other hand, transcription in eukaryotes requires additional proteins, called transcription factors, to first bind to the promoter region in the DNA template. This binding helps recruit the specific RNAP that can assemble on the DNA and start transcription.
The promoters and enhancers and their accessory proteins allow tight regulation of...
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...

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Related Experiment Video

Updated: Jun 26, 2026

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

A transcription factor affinity-based code for mammalian transcription initiation.

Molly Megraw1, Fernando Pereira, Shane T Jensen

  • 1Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708, USA.

Genome Research
|January 15, 2009
PubMed
Summary

A new model accurately predicts mammalian transcription start sites (TSS) using transcription factors (TFs) and their positional data. This approach enhances understanding of gene regulation and aids in microRNA TSS identification.

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Related Experiment Videos

Last Updated: Jun 26, 2026

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

High-throughput Purification of Affinity-tagged Recombinant Proteins
07:44

High-throughput Purification of Affinity-tagged Recombinant Proteins

Published on: August 26, 2012

Chromatin Immunoprecipitation Assay for Tissue-specific Genes using Early-stage Mouse Embryos
11:02

Chromatin Immunoprecipitation Assay for Tissue-specific Genes using Early-stage Mouse Embryos

Published on: April 29, 2011

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Large-scale cap analysis of gene expression (CAGE) data offers insights into mammalian RNA polymerase II transcription start sites (TSS).
  • A significant portion of TSS display single-peak profiles, suggesting regulation by specific sequence features.

Purpose of the Study:

  • To develop a predictive model for single-peaked TSS based on known transcription factors (TFs) and their positional enrichment.
  • To investigate the role of sequence features and TF spatial biases in TSS prediction.

Main Methods:

  • A probabilistic model was developed using TF binding data and CAGE-derived TSS locations.
  • The model was validated using cross-validation and genomic scans to assess prediction accuracy and resolution.
  • Analysis of CAGE tag clusters relative to gene 5'-ends was performed.

Main Results:

  • The developed model achieved near-perfect classification for single-peaked TSS (auROC = 0.98).
  • The model identified distinct spatial biases for numerous TFs, contributing to accurate TSS prediction.
  • A DNA code incorporating canonical features and TF biases was suggested.
  • Distinct characteristics were observed for CAGE tag clusters based on their proximity to annotated gene starts.
  • Approximately 70% of mammalian microRNA TSS were predicted using this model.

Conclusions:

  • A novel, interpretable model accurately predicts a significant subgroup of mammalian single-peaked TSS.
  • The findings highlight the importance of TF spatial distribution in promoter function and gene regulation.
  • This high-resolution model advances the prediction of TSS, including those for microRNAs.