A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve
A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve
复制标题
DOI：
10.5120/11489-7194
复制
发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
H. Saberkari;M. Shamsi;Hamed Heravi;M. Sedaaghi
中科院分区：
文献类型：
--
作者：
H. Saberkari;M. Shamsi;Hamed Heravi;M. Sedaaghi
脱氧核糖核酸(DNA)序列中蛋白质编码区的识别由于其3个碱基的周期性，一直是生物信息学中具有挑战性的问题。许多数字信号处理(DSP)技术已被应用于识别任务，主要集中在对符号DNA序列赋值，然后利用短时离散傅立叶变换(ST-DFT)等频谱分析工具来定位周期性分量。本文首先利用Z曲线法将符号DNA序列转换为数字信号，这是一种独特的展示DNA序列的三维图形，它反映了DNA序列的生物行为。然后将线性预测编码模型(LPCM)与Goertzel算法相结合，提出了一种新的DNA链外显子定位的快速算法。该算法提高了处理速度，从而降低了计算复杂度。准确地说，检测DNA序列中的小尺寸外显子是我们算法的另一个优势。在核苷酸水平上，使用(I)特异性-敏感值；(Ii)接收器工作曲线(ROC)；(Iii)ROC曲线下面积，将所提出的算法在外显子预测方面与现有的几种方法进行了比较。仿真结果表明，与其他外显子预测方法相比，该算法提高了外显子检测的准确率。在本文中，我们还开发了一个有用的用户友好的程序包来分析DNA序列。关键词序列；蛋白质编码区；信号处理；外显子；线性预测编码模型；Goertzel算法。
identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3- base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences. Keywordssequence; Protein coding regions; Signal processing; Exon; Linear predictive coding model; Goertzel algorithm.