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

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...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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

Targeted Chromosomal Sequencing of Wild Bonobos Identifies a Genetically Distinct Subpopulation East of the Lomami River.

bioRxiv : the preprint server for biology·2026
Same author

Accessible, Realistic Genome Simulation with Selection Using stdpopsim.

Molecular biology and evolution·2025
Same author

Reconstructing rearrangement phylogenies of natural genomes.

Algorithms for molecular biology : AMB·2025
Same author

Punic people were genetically diverse with almost no Levantine ancestors.

Nature·2025
Same author

Accessible, realistic genome simulation with selection using stdpopsim.

bioRxiv : the preprint server for biology·2025
Same author

Panacus: fast and exact pangenome growth and core size estimation.

Bioinformatics (Oxford, England)·2024
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Stochastic errors vs. modeling errors in distance based phylogenetic reconstructions.

Daniel Doerr1, Ilan Gronau, Shlomo Moran

  • 1Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel. moran@cs.technion.ac.il.

Algorithms for Molecular Biology : AMB
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

Phylogenetic tree reconstruction can be improved by intentionally using simpler evolutionary models. This study introduces a framework to analyze model misspecification errors, enhancing topological accuracy in phylogenetic analysis.

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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 19, 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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Distance-based phylogenetic methods rely on accurate evolutionary distance estimation.
  • Model misspecification in distance estimation can lead to systematic errors in tree reconstruction.
  • Understanding these errors is crucial for improving phylogenetic accuracy.

Purpose of the Study:

  • To develop a theoretical framework for analyzing errors in phylogenetic reconstruction due to model misspecification and sequence length.
  • To investigate the impact of assuming an oversimplified evolutionary model on topological accuracy.
  • To provide insights into why statistically inconsistent methods may outperform consistent ones.

Main Methods:

  • Introduced a theoretical framework based on 'deviation from additivity' to quantify model misspecification error.
  • Analyzed the Jukes-Cantor distance function applied to data from Kimura's two-parameter model.
  • Conducted theoretical derivations and simulation studies on quartet trees.

Main Results:

  • Demonstrated that deliberate use of oversimplified evolutionary models can increase topological accuracy.
  • Quantified the contribution of model misspecification to estimation error using the deviation from additivity.
  • Showcased the framework's utility in analyzing systematic and stochastic errors.

Conclusions:

  • Oversimplified evolutionary models can paradoxically enhance phylogenetic reconstruction accuracy.
  • The deviation from additivity framework offers new understanding of reconstruction method performance.
  • Statistically inconsistent methods can yield superior results under specific conditions.