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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cis-regulatory Sequences02:02

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Regulation of Expression at Multiple Steps01:23

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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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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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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.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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Regulation of Expression Occurs at Multiple Steps02:24

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Reporter Genes02:11

Reporter Genes

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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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Updated: Jan 14, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Reproducible processing of TCGA regulatory networks.

Viola Fanfani1, Katherine H Shutta1,2, Panagiotis Mandros1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115  USA.

Gigascience
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces tcga-data-nf, a Nextflow workflow for reproducible cancer regulatory network analysis from The Cancer Genome Atlas (TCGA) data. It integrates multiomic data processing and network inference, providing a valuable tool for cancer research.

Keywords:
NetworkDataCompanionNextflowThe Cancer Genome Atlascancergene regulatory networkreproducibility

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Cancer Genomics

Background:

  • Advances in sequencing and computation enable deep molecular disease analysis.
  • Biological networks are crucial for omics data analysis and modeling gene/protein interactions.
  • The Cancer Genome Atlas (TCGA) provides extensive data but lacks a unified workflow for end-to-end regulatory network analysis.

Purpose of the Study:

  • To develop a coherent and reusable workflow for reproducible inference and analysis of cancer regulatory networks.
  • To integrate existing computational tools into a robust pipeline for TCGA data.

Main Methods:

  • Developed tcga-data-nf, a Nextflow workflow for reproducible regulatory network inference from TCGA data.
  • The workflow integrates multiomic data (RNA sequencing, methylation) downloading, preprocessing, and analysis using Network Zoo.
  • Powered by the NetworkDataCompanion R package for TCGA data management.

Main Results:

  • tcga-data-nf enables reproducible regulatory network inference from thousands of TCGA samples via a single command.
  • Demonstrated pipeline utility in investigating epigenetic differences in colon cancer subtypes.
  • Generated a public database of pre-computed networks for 10 common cancer types.

Conclusions:

  • tcga-data-nf offers a complete, flexible, and extensible framework for cancer regulatory network analysis.
  • Addresses the need for reproducible workflows in analyzing TCGA data.
  • Bridges a gap in current software tools for TCGA data analysis.