Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Speciation Rates

Speciation Rates

Speciation can proceed at markedly different rates, and evolutionary biologists commonly describe these differences through the models of gradualism and punctuated equilibrium. Both patterns explain how new species arise, but they differ in the tempo and continuity of evolutionary change. In both cases, evolutionary change arises from heritable variation within populations, with natural selection often shaping traits that improve survival and reproduction under specific environmental conditions.

Gene Evolution - Fast or Slow?

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?

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...

Evolution of New Traits in Microbes

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...

Convergent Evolution

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

You might also read

Related Articles

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

Sort by

Same author

Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania.

Proceedings of the National Academy of Sciences of the United States of America·2023

Same author

Carbon emissions from the global land rush and potential mitigation.

Nature food·2023

Same author

Computational Creativity and Aesthetics with Algorithmic Information Theory.

Entropy (Basel, Switzerland)·2021

Same author

On the Problem of Small Objects.

Entropy (Basel, Switzerland)·2021

Same author

Mean and Variance of Phylogenetic Trees.

Systematic biology·2019

Same author

Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity.

Ecology·2016

Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

FEAST: sensitive local alignment with multiple rates of evolution.

Alexander K Hudek¹, Daniel G Brown

¹David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada. akhudek@cs.uwaterloo.ca

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|August 25, 2010

Summary

This summary is machine-generated.

FEAST, a new local aligner, enhances homologous subsequence identification using a sensitive extension algorithm and probabilistic model. This approach improves alignment sensitivity and specificity for genomic sequences.

More Related Videos

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

Related Experiment Videos

Last Updated: Jun 9, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate identification of homologous subsequences is crucial for comparative genomics.
Existing local alignment tools face challenges in balancing sensitivity and specificity.

Purpose of the Study:

To introduce FEAST, a novel pairwise local aligner.
To improve the sensitivity and specificity of homologous subsequence identification.
To develop a new procedure for training alignment parameters.

Main Methods:

Developed a sensitive extension algorithm considering evolutionary histories.
Implemented a descriptive probabilistic alignment model with multiple submodels.
Introduced a new parameter training procedure applied to human and mouse genomes.

Main Results:

FEAST's extension algorithm achieves higher maximum sensitivity and better specificity balance than Viterbi extensions.
The probabilistic model effectively describes homologous DNA regions.
Parameter training using two submodels yielded superior alignments.
FEAST achieved 0.59 sensitivity on synthetic tests, outperforming LASTZ (0.35).

Conclusions:

FEAST offers a significant advancement in local alignment for genomic data.
The new extension algorithm and probabilistic model enhance the accuracy of identifying homologous sequences.
The developed parameter training procedure optimizes alignment performance.