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Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

Lavanya Rishishwar1, Bhasker Pant, Kumud Pant

  • 1School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332, USA. lavanya.rishishwar@gatech.edu

Genomics, Proteomics & Bioinformatics
|December 27, 2011
PubMed
Summary

This study identifies genomic patterns in Mycobacterium tuberculosis (MTB) for gene function prediction. A developed model accurately characterizes MTB genes, aiding in tuberculosis research and drug development.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microbiology

Background:

  • Tuberculosis, caused by Mycobacterium tuberculosis (MTB), remains a significant global health challenge.
  • Effective control requires understanding MTB's genetic makeup and gene functions.
  • Current research employs diverse wet-lab and dry-lab methods for MTB studies.

Purpose of the Study:

  • To mine genomic patterns for in silico functional gene characterization within the MTB complex.
  • To develop and validate a predictive model for MTB gene function.
  • To assess the model's generalization capability across different MTB strains and related species.

Main Methods:

  • Genomic pattern mining from Mycobacterium tuberculosis (MTB) complex strains.
  • Development of a predictive model based on identified genomic patterns.
  • In silico validation of the model using MTB strain H37Rv and other MTB strains, including M. bovis.
  • Analysis of GC content and dinucleotide composition across the MTB genome.

Main Results:

  • The developed model achieved 99.77% prediction accuracy for MTB strain H37Rv genes.
  • The model demonstrated a mean prediction accuracy of 85.76% across four other MTB strains and M. bovis.
  • Consistent GC content throughout the genome suggested the absence of horizontally transferred pathogenicity islands.
  • Dinucleotide composition was identified as an effective discriminator for functional gene classes in the MTB complex.

Conclusions:

  • Dinucleotide composition serves as an efficient functional class discriminator for the Mycobacterium tuberculosis complex.
  • The developed predictive model and web server (Tuber-Gene) facilitate in silico functional gene characterization.
  • This approach aids in advancing tuberculosis research and understanding MTB genomics.