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Related Experiment Video

Updated: Jun 14, 2026

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

A benchmark of parametric methods for horizontal transfers detection.

Jennifer Becq¹, Cécile Churlaud, Patrick Deschavanne

¹Dynamique des Structures et Interactions des Macromolécules Biologiques, Institut National de la Santé et de la Recherche Médicale UMR-S 665, Université Paris Diderot, Institut National de la Transfusion Sanguine, Paris, France.

|April 9, 2010

Summary

Detecting horizontal gene transfer (HGT) in prokaryotes is crucial for evolution studies. This benchmark evaluated 16 methods, finding that combining gene-based and window-based approaches offers the best accuracy for HGT detection.

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Experimental Methods to Study Human Postural Control

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Published on: September 11, 2019

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Genomics
Evolutionary Biology
Bioinformatics

Background:

Horizontal gene transfer (HGT) significantly impacts prokaryotic evolution.
Numerous computational methods exist to detect HGTs using genomic data.
Inconsistencies in results across different detection methods have been reported.

Purpose of the Study:

To benchmark and compare the efficiency of 16 parametric HGT detection methods.
To identify the most effective methods and metrics for HGT detection.
To propose a combined approach for improved HGT detection accuracy.

Main Methods:

Utilized artificial genomes with controlled HGT parameters for method evaluation.
Assessed 16 representative parametric HGT detection methods.
Analyzed method performance based on sensitivity, specificity, and error rates using criteria like tetranucleotides and codon usage.

Main Results:

Significant variation in method efficiencies was observed.
Tetranucleotide frequency (window methods) and codon usage (gene-based methods) with Kullback-Leibler divergence were most effective.
Window methods showed high sensitivity but low specificity, while gene-based methods were specific but lacked sensitivity.

Conclusions:

No single method is universally optimal for detecting all types of HGTs.
Combining gene-based methods (for specificity) with window-based methods (for sensitivity) is recommended for robust HGT detection.
This hybrid approach leverages the strengths of different detection strategies.