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Related Experiment Videos

Detecting horizontal gene transfer with T-REX and RHOM programs.

Zuofeng Li1, Li Wang, Yang Zhong

  • 1School of Life Sciences, Fudan University, Shanghai 200433, China and Shanghai Center for Bioinformation Technology, China.

Briefings in Bioinformatics
|January 20, 2006
PubMed
Summary

Detecting horizontal gene transfer (HGT) is crucial with abundant genomic data. This review covers T-REX and RHOM, bioinformatics freeware tools aiding in HGT network reconstruction and detection.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • The Human Genome Project and high-throughput sequencing generate vast biological data.
  • Detecting horizontal gene transfer (HGT) is an emerging and important field in bioinformatics.
  • Existing methods for HGT detection require efficient computational tools.

Purpose of the Study:

  • To review two freeware programs, T-REX and RHOM, for detecting horizontal gene transfer (HGT).
  • To highlight the functionalities and strengths of each program for HGT analysis.
  • To provide researchers with accessible tools for analyzing genomic data for HGT.

Main Methods:

  • Description of T-REX: A graphical user interface (GUI) program for MS Windows.
  • Description of RHOM: A set of command-line driven programs for Linux.

Related Experiment Videos

  • Both tools utilize gene and species distance matrices for HGT network reconstruction and detection.

    • Main Results:

    • T-REX offers a user-friendly interface for reconstructing HGT networks.
    • RHOM provides an extensive statistical framework and graphical display for HGT detection.
    • T-REX visualizes the reticulation network, while RHOM displays sequence position probabilities for candidate genes.

    Conclusions:

    • T-REX and RHOM are valuable freeware tools for HGT detection and analysis in bioinformatics.
    • The choice between T-REX and RHOM depends on user preference for GUI vs. command-line and specific analytical needs.
    • These tools facilitate the study of HGT in the context of large-scale genomic data.