Saini Shiwani, Dewan Lillie
Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119 India.
Springerplus. 2016 Jan 22;5:64. doi: 10.1186/s40064-016-1668-9. eCollection 2016.
This paper highlights the potential of discrete wavelet transforms in the analysis and comparison of genomic sequences of Mycobacterium tuberculosis (MTB) with different resistance characteristics. Graphical representations of wavelet coefficients and statistical estimates of their parameters have been used to determine the extent of similarity between different sequences of MTB without the use of conventional methods such as Basic Local Alignment Search Tool. Based on the calculation of the energy of wavelet decomposition coefficients of complete genomic sequences, their broad classification of the type of resistance can be done. All the given genomic sequences can be grouped into two broad categories wherein the drug resistant and drug susceptible sequences form one group while the multidrug resistant and extensive drug resistant sequences form the other group. This method of segregation of the sequences is faster than conventional laboratory methods which require 3-4 weeks of culture of sputum samples. Thus the proposed method can be used as a tool to enhance clinical diagnostic investigations in near real-time.
本文强调了离散小波变换在分析和比较具有不同耐药特性的结核分枝杆菌(MTB)基因组序列方面的潜力。小波系数的图形表示及其参数的统计估计已被用于确定MTB不同序列之间的相似程度,而无需使用诸如基本局部比对搜索工具等传统方法。基于完整基因组序列小波分解系数能量的计算,可以对耐药类型进行大致分类。所有给定的基因组序列可分为两大类,其中耐药和药物敏感序列形成一组,而耐多药和广泛耐药序列形成另一组。这种序列分离方法比需要3 - 4周痰标本培养的传统实验室方法更快。因此,所提出的方法可作为一种工具,用于近乎实时地加强临床诊断研究。