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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Vijaykumar Yogesh Muley1,2

  • 1Independent Researcher, Hingoli, India. vijaykumar.muley@outlook.de.

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2023
PubMed
Summary

This study introduces an R/RStudio protocol for deep learning in gene regulatory network reconstruction. It empowers biologists without programming expertise to predict transcription factor-gene interactions genome-wide.

Keywords:
Deep learningGene regulationGene regulatory networks (GRN)TensorFlowTranscription factorsTranscriptional regulatory networks

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Deep learning excels at complex biological problems like gene regulatory network reconstruction.
  • Current deep learning tools require programming skills, limiting biologist accessibility.
  • Gene regulatory networks involve transcription factors and their target genes.

Purpose of the Study:

  • To present an accessible deep learning protocol for biologists using R/RStudio.
  • To enable genome-wide prediction of transcription factor-gene regulatory interactions.
  • To lower the barrier for applying advanced computational methods in biological research.

Main Methods:

  • Utilized TensorFlow and Keras API within R/RStudio.
  • Developed a protocol for data preprocessing, neural network design, and training.
  • Employed publicly available gene expression data and benchmarks for validation.

Main Results:

  • Successfully predicted genome-wide regulatory interactions between transcription factors and genes.
  • Provided insights into deep learning model parameter tuning.
  • Demonstrated the protocol's effectiveness for novel regulatory association forecasting.

Conclusions:

  • The protocol makes deep learning accessible for predicting gene regulatory networks.
  • Researchers can gain practical experience applying deep learning to biological data.
  • The protocol is adaptable for various research questions in computational biology.