Jove
Visualize
Contact Us

Related Concept Videos

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,...
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...
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...
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...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Double-negative (CD27<sup>-</sup>IgD<sup>-</sup>) B cells are expanded in NSCLC and inversely correlate with affinity-matured B cell populations.

Journal of translational medicine·2018
Same author

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

BMC medical genomics·2014
Same author

Alisertib added to rituximab and vincristine is synthetic lethal and potentially curative in mice with aggressive DLBCL co-overexpressing MYC and BCL2.

PloS one·2014
Same author

In Vitro Assessment of the Inflammatory Breast Cancer Cell Line SUM 149: Discovery of 2 Single Nucleotide Polymorphisms in the RNase L Gene.

Journal of Cancer·2013
Same author

Choosing a method for phylogenetic prediction.

CSH protocols·2011
Same author

Maximum parsimony method for phylogenetic prediction.

CSH protocols·2011
Same journal

Identification of positive GATEWAY expression clones when both the pENTRY and pDEST vectors contain the same marker for bacterial selection.

CSH protocols·2012
Same journal

Imaging protein interactions by FRET microscopy: cell preparation for FRET analysis.

CSH protocols·2012
Same journal

Imaging protein interactions by FRET microscopy: labeling proteins with fluorescent dyes.

CSH protocols·2012
Same journal

Bradford assay.

CSH protocols·2012
Same journal

Detection of ubiquitylated proteins in mammalian cells.

CSH protocols·2012
Same journal

Imaging of organelle membrane systems and membrane traffic in living cells.

CSH protocols·2012
See all related articles
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 Experiment Video

Updated: Jun 4, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

The maximum likelihood approach for phylogenetic prediction.

David W Mount

    CSH Protocols
    |March 2, 2011
    PubMed
    Summary
    This summary is machine-generated.

    Maximum likelihood methods are valuable for phylogenetic prediction with varied sequence alignments. These methods adjust evolutionary models to best fit observed sequence variations in multiple sequence alignments.

    More Related Videos

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
    08:57

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

    Published on: August 14, 2018

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
    07:49

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

    Published on: August 16, 2017

    Related Experiment Videos

    Last Updated: Jun 4, 2026

    A Practical Guide to Phylogenetics for Nonexperts
    12:00

    A Practical Guide to Phylogenetics for Nonexperts

    Published on: February 5, 2014

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
    08:57

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

    Published on: August 14, 2018

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
    07:49

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

    Published on: August 16, 2017

    Area of Science:

    • Computational Biology
    • Evolutionary Biology
    • Bioinformatics

    Background:

    • Phylogenetic prediction is crucial for understanding evolutionary relationships.
    • Traditional methods may struggle with high sequence variability.
    • Maximum likelihood (ML) offers a robust approach for phylogenetic inference.

    Purpose of the Study:

    • To highlight the utility of maximum likelihood methods in phylogenetic prediction.
    • To explain the fundamental principles of ML in sequence analysis.
    • To compare ML with other phylogenetic methods like maximum parsimony.

    Main Methods:

    • Utilizing maximum likelihood (ML) models for phylogenetic analysis.
    • Employing models of evolutionary rates for nucleic acid or protein sequences.
    • Analyzing sequence variations within columns of a multiple sequence alignment (MSA).

    Main Results:

    • ML methods effectively handle significant variation within MSAs.
    • The iterative adjustment of models ensures a best fit to observed data.
    • ML analysis, like maximum parsimony, operates on a column-by-column basis of the MSA.

    Conclusions:

    • Maximum likelihood methods provide a powerful framework for phylogenetic inference, especially with diverse sequence data.
    • The adaptability of ML models to observed variations enhances their reliability.
    • ML methods represent a significant advancement in analyzing evolutionary patterns from sequence data.