Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Phylogeny01:23

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire...
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,...
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...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Polymorphisms in drug-metabolizing enzymes as modifiers of cancer risk.

Clinical chemistryยท1995
Same author

CD30-mediated signaling promotes the development of human T helper type 2-like T cells.

The Journal of experimental medicineยท1995
Same author

Objective measurement of the benefit of walking sticks in peripheral vestibular balance disorders, using the Sway Weigh balance platform.

The Journal of laryngology and otologyยท1995
Same author

Arthritis and perceptions of quality of life: an examination of positive and negative affect in rheumatoid arthritis patients.

Health psychology : official journal of the Division of Health Psychology, American Psychological Associationยท1995
Same author

Diastolic time: an important determinant of regional arterial blood flow.

The American journal of physiologyยท1995
Same author

Are we vaccinating too much?

Journal of the American Veterinary Medical Associationยท1995

Related Experiment Video

Updated: Jul 16, 2026

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication
09:52

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

Published on: September 20, 2016

Expectation-maximization method for reconstructing tumor phylogenies from single-cell data.

G Pennington1, C A Smith, S Shackney

  • 1Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|March 21, 2007
PubMed
Summary

This study introduces a computational method to analyze tumor heterogeneity, revealing distinct molecular pathways in cancers. This approach aids in understanding cancer progression and developing targeted therapies.

More Related Videos

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
05:58

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Published on: August 16, 2024

Related Experiment Videos

Last Updated: Jul 16, 2026

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication
09:52

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

Published on: September 20, 2016

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
05:58

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Published on: August 16, 2024

Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Cancerous tumors with similar clinical symptoms can exhibit significant molecular differences.
  • Understanding these molecular differences is crucial for accurate prognoses and targeted therapies.
  • Tumor heterogeneity, with cells at various progression stages, complicates molecular characterization.

Purpose of the Study:

  • To develop a computational approach to characterize tumor progression pathways by leveraging tumor heterogeneity.
  • To infer evolutionary sequences of cancer cells within individual tumors using single-cell assays.
  • To create a comprehensive profile of common cancer pathways across patient populations.

Main Methods:

  • Utilizing phylogenetic algorithms to infer likely evolutionary sequences from single-cell data.
  • Integrating expectation maximization to estimate unknown parameters in phylogenetic analysis.
  • Applying the method to fluorescent in situ hybridization (FISH) data from breast cancer samples.

Main Results:

  • The computational approach successfully inferred tumor progression pathways from single-cell data.
  • Results demonstrated consistency with existing findings in breast cancer research.
  • Novel insights into the mechanisms driving tumor progression were uncovered.

Conclusions:

  • The proposed computational method effectively analyzes tumor heterogeneity for pathway characterization.
  • This approach enhances our understanding of cancer evolution and molecular subtypes.
  • The findings pave the way for improved diagnostic and therapeutic strategies in oncology.