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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.1K
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...
7.1K
Phylogenetic Trees03:21

Phylogenetic Trees

45.3K
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.
45.3K

You might also read

Related Articles

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

Sort by
Same author

Contradictive-objective-solution (C-OS) matrix research for technology gap analysis of electrolytic copper foil.

Scientific reports·2026
Same author

NeuroStream: an interactive platform for exploratory visualization and harmonization of multicohort brain MRI data.

Bioinformatics advances·2026
Same author

Quaternion-based CNN for heart rate prediction from PPG.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Targeting serine metabolism boosts antimycobacterial immunity during Mycobacterium tuberculosis H37Rv infection.

Molecules and cells·2026
Same author

<i>Philadelphus tenuifolius</i> Leaf Extract Exhibits Anti-Tuberculosis Activity by Enhancing Host Autophagy and Immunity: A Promising Host-Directed Therapeutic Candidate.

Journal of microbiology and biotechnology·2026
Same author

Synergistic effect of combined hyperthermia and radiotherapy mediated by AQP5 reduction in pancreatic cancer.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group·2026

Related Experiment Video

Updated: Jun 25, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

15.9K

Utilization of systematic error-assessment software to improve phylogenetic accuracy.

Hayeon Kim1,2,3, Junghwan Lee2, Mikyeong Je4

  • 1Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.

Journal of Bioinformatics and Computational Biology
|May 30, 2024
PubMed
Summary

Modern phylogenetic analyses increasingly rely on genetic data. This study identifies systematic biases in large datasets to improve the accuracy and reliability of evolutionary relationship estimations using bioinformatics.

Keywords:
Phylogenetic analysisbioinformaticssystematic error

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.3K
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

7.1K

Related Experiment Videos

Last Updated: Jun 25, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

15.9K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.3K
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

7.1K

Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Genetics

Background:

  • Classical phylogenetics used morphological data; modern approaches utilize genetic information for phylogenetic tree construction.
  • Current research in phylogenetics focuses on improving the accuracy and reliability of evolutionary relationship estimations.
  • While stochastic errors have decreased in large-scale phylogenetic datasets, systematic errors have become more prevalent.

Purpose of the Study:

  • To assess systematic error in phylogenetic datasets and enhance the accuracy of phylogenetic tree construction.
  • To propose a method for identifying suitable gene markers and resolving conflicting phylogenetic topologies by analyzing systematic biases.
  • To improve the reliability of phylogenetic software for more accurate evolutionary estimations.

Main Methods:

  • Analysis of three distinct datasets (Terebelliformia, Daphniid, and Glires clades) using bioinformatics software.
  • Systematic error assessment to identify and quantify biases within the datasets.
  • Development of a bias combination strategy to discern optimal gene markers and address conflicting topologies.

Main Results:

  • Demonstrated the increased probability of systematic errors in large-scale phylogenetic datasets.
  • Successfully assessed systematic error and improved phylogenetic tree accuracy using bioinformatics tools.
  • Proposed a novel approach combining systematic biases to select appropriate gene markers and resolve phylogenetic conflicts.

Conclusions:

  • Understanding and addressing systematic bias is crucial for accurate phylogenetic tree construction.
  • The proposed method aids in selecting the most suitable gene markers for specific taxa.
  • Findings contribute to enhancing the reliability of phylogenetic software for more precise evolutionary relationship estimations.