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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 in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Regulation of Expression Occurs at Multiple Steps02:24

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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

Nelson Kibinge1, Naoaki Ono1, Masafumi Horie2

  • 1Graduate School of Information Science, Nara Institute of Science and Technology, Takayama 8916-5, Ikoma, Nara 630-0192, Japan.

Journal of Biomedical Informatics
|April 12, 2016
PubMed
Summary

This study introduces TransReguloNet, a unified pipeline for analyzing gene expression data to understand transcription factor regulation. It integrates network construction and data mining for improved biological interpretation and hypothesis generation.

Keywords:
Biological interpretationGene regulationPathway-based modularizationTranscription factors

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Traditional gene expression analysis involves separate steps for data mining and network inference.
  • A unified framework is needed to streamline interpretation and hypothesis generation in transcription regulation studies.

Purpose of the Study:

  • To develop an integrated computational workflow for analyzing transcription factor-driven gene regulation.
  • To enhance biological interpretation and facilitate hypothesis generation by merging network construction with gene expression data mining.

Main Methods:

  • A novel pipeline merging differential expression, network construction, pathway-based modularization, clustering, and visualization.
  • Implementation as a web application (TransReguloNet) for pathway visualization and comparison of transcription factor activity across sample conditions.

Main Results:

  • The pipeline successfully integrates multiple analytical steps into a singular framework.
  • Pathway-based modularization improves the biological interpretability of gene expression profiles.
  • TransReguloNet enables effective comparison of transcription factor activity between different experimental conditions.

Conclusions:

  • The proposed integrated workflow and TransReguloNet software offer a powerful tool for transcription regulation network analysis.
  • This approach enhances the understanding of gene regulation mechanisms in complex biological systems, including cancer.