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Related Concept Videos

Transcription Factors02:16

Transcription Factors

82.4K
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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Transcription Elongation Factors02:35

Transcription Elongation Factors

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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
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Transcription Elongation Factors02:35

Transcription Elongation Factors

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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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Cooperative Binding of Transcription Regulators02:13

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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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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from

Daniel Quang1, Xiaohui Xie1

  • 1University of California, Department of Computer Science, Irvine, CA 92697, United States.

Methods (San Diego, Calif.)
|March 30, 2019
PubMed
Summary
This summary is machine-generated.

FactorNet, a novel deep learning model, predicts transcription factor binding sites across cell types. This computational approach addresses experimental limitations, enabling broader insights into gene regulation.

Keywords:
DREAMDeep learningENCODETranscription factors

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Experimental determination of transcription factor (TF) binding profiles is infeasible for all TF/cell type combinations.
  • A significant gap exists in understanding TF binding across diverse cellular contexts.

Purpose of the Study:

  • To develop a computational method for imputing missing TF binding data.
  • To predict TF binding profiles in untested cell types using a deep learning approach.

Main Methods:

  • Developed FactorNet, a convolutional-recurrent neural network.
  • Leveraged genomic sequences, annotations, gene expression, and signal data (e.g., DNase I cleavage).
  • Implemented strategies to optimize runtime and memory usage.

Main Results:

  • FactorNet achieved high accuracy in predicting TF binding sites.
  • The model ranked first in six out of 13 pairs in the ENCODE-DREAM challenge.
  • Network visualization enabled interpretation of binding prediction mechanisms.

Conclusions:

  • FactorNet offers a scalable solution for predicting TF binding data.
  • The study provides insights into factors influencing cross-cell type prediction accuracy.
  • Publicly available code facilitates reproducibility and further research.