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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Synteny and Evolution02:31

Synteny and Evolution

John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral chromosome underwent...

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Related Experiment Video

Updated: May 31, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Ankur P Parikh1, Wei Wu, Ross E Curtis

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

Estimating gene regulatory networks across cell lineages is challenging due to branching. Treegl, a novel algorithm, effectively infers these evolving networks, outperforming existing methods and offering insights into cancer progression.

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Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Last Updated: May 31, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

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Published on: May 28, 2021

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Estimating gene regulatory networks in evolving cell lineages is crucial for understanding development and differentiation.
  • Cellular evolution involves contiguous changes and branching, forming tree-like structures of networks.
  • Existing methods struggle with the complexity of inferring networks from branched biological lineages.

Purpose of the Study:

  • To develop a novel algorithm for estimating multiple gene regulatory networks across cell types related by a tree-genealogy.
  • To leverage similarities between related networks while identifying sharp differences.
  • To provide a tool for analyzing gene regulatory network evolution in biological systems.

Main Methods:

  • Proposed Treegl, an ℓ(1) plus total variation penalized linear regression method.
  • Algorithm designed to estimate multiple gene networks from limited samples across cell types.
  • Utilizes the tree-genealogy structure to inform network inference.

Main Results:

  • Treegl significantly outperforms existing methods in simulations.
  • The algorithm successfully identifies distinct network structures in related cell types.
  • Application to a breast cancer dataset yielded biologically valid insights into cancer cell progression and reversion.

Conclusions:

  • Treegl is an effective method for inferring gene regulatory networks in evolving cell populations.
  • The algorithm provides valuable insights into complex biological processes like cancer progression.
  • The developed software will be publicly available for further research.