School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Genomics Proteomics Bioinformatics. 2011 Oct;9(4-5):171-8. doi: 10.1016/S1672-0229(11)60020-X.
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://www.bifmanit.org/tb2/.
结核分枝杆菌（MTB）是引起结核病的病原体，是本世纪最可怕的疾病之一。长期以来，世界各地的研究人员一直使用各种湿实验室和干实验室技术对其进行研究。在这项研究中，我们专注于挖掘基因组水平上的有用模式，这些模式可应用于 MTB 复合体基因的计算功能特征描述。基于在这项研究中发现的模式开发的模型可以正确识别 MTB 菌株 H37Rv 基因组中 99.77%的输入基因。该模型经过另外四种 MTB 菌株和同源物 M. bovis 的测试，以进一步评估其泛化能力。平均预测准确率为 85.76%。还观察到整个基因组中的 GC 含量相当稳定，这表明没有任何从其他生物体转移来的致病性岛。这项研究表明二核苷酸组成是 MTB 复合体的有效功能分类鉴别器。为了方便该模型的应用，开发了一个名为 Tuber-Gene 的网络服务器，可以在 http://www.bifmanit.org/tb2/ 上免费访问。
