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Mining Genomic Patterns in Mycobacterium tuberculosis H37Rv Using a Web Server Tuber-Gene

Genomics, Proteomics & Bioinformatics 2011年第4期9卷 171-178页

作者： Lavanya Rishishwar bhasker pant Kumud pant Kamal R. Pardasani School of Biology Georgia Institute of Technology Atlanta Georgia 30332 USA Department of Information Technology Graphic Era University Dehradun Uttrakhand 248002 India Department of Bioinformatics Maulana Azad National Institute of Technology Bhopal Madhya Pradesh 462051 India Department of Mathematics Maulana Azad National Institute of Technology Bhopal Madhya Pradesh 462051 India

Mycobacterium tuberculosis （MTB）, causative agent of tuberculosis, is one of the most dreaded diseases of the century. It has long been studied by researchers throughout the world using various wet-lab and dry-lab techniques In this study, we focus on mining useful patterns at genomic level that can be applied for in silico functional characterization of genes from the MTB complex. The model developed on the basis of the patterns found in this study can correctly identify 99.77% of the input genes from the genome of MTB strain H37Rv. The model was tested against four other MTB strains and the homologue M. bovis to further evaluate its generalization capability. The mean prediction accuracy was 85.76%. It was also observed that the GC content remained fairly constant throughout the genome, implicating the absence of any pathogenicity island transferred from other organisms. This study reveals that dinucleotide composition is an efficient functional class discriminator for MTB complex. To facilitate the application of this model, a web server Tuber-Gene has been developed, which can be freely accessed at http：//***/tb2/.

关键词： dinucleotide composition Mycobacterium tuberculosis support vector machine data mining

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