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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: May 27, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets.

Daniel Hurley1, Hiromitsu Araki, Yoshinori Tamada

  • 1Auckland Bioengineering Institute, Department of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

Nucleic Acids Research
|November 29, 2011
PubMed
Summary

We developed user-friendly tools for analyzing gene regulatory networks from RNA data, making complex bioinformatics accessible to more researchers for biological discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
  • Inferring GRNs from RNA abundance data is of significant interest but challenging.
  • Current methods are often underutilized due to complexity and reliance on bioinformaticians.

Purpose of the Study:

  • To present a suite of user-friendly tools for analyzing gene regulatory networks.
  • To demonstrate the application of these tools using human endothelial cell microarray data.
  • To discuss the strengths and limitations of RNA abundance-based network inference.

Main Methods:

  • Development of a toolkit for gene network analysis.
  • Application of the toolkit to microarray datasets from human endothelial cells.
  • Inference and visualization of gene regulatory networks.

Main Results:

  • Successfully inferred various regulatory networks from RNA abundance data.
  • Illustrated the practical application of the developed tools.
  • Provided insights into the capabilities and constraints of network inference methods.

Conclusions:

  • The developed tools facilitate the analysis of gene regulatory networks.
  • RNA abundance data can be effectively used for network inference with appropriate tools.
  • Encouraging collaboration with researchers to utilize these tools for biological questions.