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 Experiment Videos

Towards identifying lateral gene transfer events.

L Addario-Berry1, M Hallett, J Lagergren

  • 1McGill Centre for Bioinformatics, McGill University, Montréal, Canada. laddar@mcb.mcgill.ca

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2003
PubMed
Summary

This study evaluates an algorithm for detecting lateral gene transfers. The efficient algorithm accurately estimates transfer events in simulated gene and species trees, with low error rates and few optimal scenarios for realistic sizes.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Predictive factors for prolonged nutritional support after oesophagogastric cancer resection.

Annals of the Royal College of Surgeons of England·2026
Same author

In-hospital complications after gastrectomy in relation to long-term survival in gastric adenocarcinoma: a population-based cohort study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2025
Same author

Prehabilitation of Patients With Oesophageal Malignancy Undergoing Peri-Operative Treatment (Pre-EMPT): Outcomes From a Prospective Controlled Trial.

Journal of surgical oncology·2025
Same author

Exercise During Chemotherapy for Cancer: A Systematic Review.

Journal of surgical oncology·2024
Same author

Multiple staging investigations may not change management in patients with high-grade dysplasia or early esophageal adenocarcinoma.

Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus·2023
Same author

Capsular inflammation after immediate breast reconstruction - Gene expression patterns and inflammatory cell infiltration in irradiated and non-irradiated breasts.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2022

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Lateral gene transfer (LGT) is a significant evolutionary mechanism.
  • Accurate detection of LGT events is crucial for understanding genome evolution.
  • Existing methods may face challenges with complex evolutionary histories.

Purpose of the Study:

  • To evaluate the performance of a specific model and algorithm for detecting lateral gene transfer events.
  • To assess the algorithm's accuracy and efficiency on simulated biological data.
  • To determine the feasibility of the algorithm for realistic biological datasets.

Main Methods:

  • Simulated gene and species trees using a Poisson process for transfer event timing.
  • Implementation of an efficient algorithm to estimate the minimum number of LGT events.

Related Experiment Videos

  • Performance evaluation based on error rates, variance, and the number of optimal scenarios.

    • Main Results:

    • The algorithm successfully handles realistic instance sizes.
    • Low mean error and variance were observed when evolutionary saturation was absent.
    • The number of optimal scenarios is surprisingly low for realistic input sizes.
    • The framework is expected to perform well in practice, as undetectable evolutionary events are rare.

    Conclusions:

    • The evaluated algorithm is efficient and accurate for detecting lateral gene transfers.
    • The simulation framework provides a robust method for assessing phylogenetic inference tools.
    • The findings support the practical applicability of the algorithm in evolutionary studies.