基于信号处理理论和方法的基因预测研究
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摘要
基因是遗传的基本单位,是携带遗传信息的DNA片断,而非基因部分不编码蛋白质,因此,从DNA序列中预测出基因区一直是生物信息学的重要研究内容。本文运用信号处理的理论和方法,包括变换域方法、数字滤波器、时频分析、统计学习和智能算法等来研究基因预测。
首先分析了基因预测滤波器的原理,提出编码区序列的长度和周期性的强弱是影响预测结果的重要因素。根据基因编码区的周期性质,设计了具有窄带选通特性的FIR数字滤波器和自适应滤波器。利用已标注的基因数据进行计算,获得了时域外显子位置的预测曲线,说明所设计的滤波器是有效的,并且能够提高基因预测的准确性。
其次将基因预测的滤波器与傅立叶变换相结合,提出了一种用于基因识别的改进傅立叶方法。该方法能放大周期3信号,滤除背景噪声,不受现有傅立叶方法对于序列长度的限制。实验表明改进的傅立叶方法提高了预测精度。同时,给出一种加窗傅立叶变换的方法,可以识别出DNA序列中的编码区和非编码区。
然后将基因编码区的隐马尔科夫模型与前向算法相结合,实现了对外显子的识别。对已标注的DNA序列进行预测,该算法既有效,又降低了计算量。同时,将支持向量机用于基因分类,实验表明该方法不仅提高了预测精度,而且降低了训练所需的数据。
最后分别研究了四种特征量和三种判别方法对算法预测精度的影响,在此基础上,实现了一种基于多种特征量的基因识别算法,实验结果表明文中给出的算法进一步弥补了傅立叶方法的不足,对于较短的基因序列,其预测精度高于现有的基因识别算法。
Gene is the basic unit of heredity and is DNA segment with genetic information, but non-gene can not code protein, so finding genes in DNA sequences has been an important problem in bioinformatics for a long time. In this dissertation, theory and methods of signal processing including transform domain, digital filter, time-frequency analysis, statistical learning and intelligent algorithms are applied to identify genes.
Firstly, theory of filter for gene prediction is analyzed, therefore two important elements are proposed: length and weak/strong periodicity of protein coding regions. According to the periodicity of the coding regions, the FIR filter and the adaptive filters with narrow pass-band are developed. The predicted locations of the exons are achieved by calculating the annotated gene sequence. The experimental results indicate that the designed filters are valid and can improve accuracy of gene identification.
Secondly, an improved Fourier transform approach is proposed by integrating the gene prediction filter with the Fourier transform. This algorithm can magnify period-3 signals, remove the background noises, and is not restricted by the length of the predicted sequences unlike the existing Fourier methods. The experimental results show that the improved Fourier method can promote predictive accuracy. At the same time, the Fourier method based on sliding window is applied to identify the coding and noncoding regions in DNA sequences.
Thirdly, the forward algorithm integrated with the Hidden Markov Model of coding regions is applied to predict exons in genes. By identifying the annotated gene sequence the designed algorithm is valid and reduces the computational complication. At the same time, the algorithm for gene classification based on support vector machine is schemed. The experimental results show that the proposed method can not only improve accuracy, but also reduce training data.
At last, four features and three discriminate analysis methods are studied for improving predictive accuracy, and the gene identification algorithm based on multiple features is proposed. The experimental results indicate that the developed algorithm can improve the Fourier methods and has better accuracy than the existing gene prediction method for short DNA sequences.
首先分析了基因预测滤波器的原理,提出编码区序列的长度和周期性的强弱是影响预测结果的重要因素。根据基因编码区的周期性质,设计了具有窄带选通特性的FIR数字滤波器和自适应滤波器。利用已标注的基因数据进行计算,获得了时域外显子位置的预测曲线,说明所设计的滤波器是有效的,并且能够提高基因预测的准确性。
其次将基因预测的滤波器与傅立叶变换相结合,提出了一种用于基因识别的改进傅立叶方法。该方法能放大周期3信号,滤除背景噪声,不受现有傅立叶方法对于序列长度的限制。实验表明改进的傅立叶方法提高了预测精度。同时,给出一种加窗傅立叶变换的方法,可以识别出DNA序列中的编码区和非编码区。
然后将基因编码区的隐马尔科夫模型与前向算法相结合,实现了对外显子的识别。对已标注的DNA序列进行预测,该算法既有效,又降低了计算量。同时,将支持向量机用于基因分类,实验表明该方法不仅提高了预测精度,而且降低了训练所需的数据。
最后分别研究了四种特征量和三种判别方法对算法预测精度的影响,在此基础上,实现了一种基于多种特征量的基因识别算法,实验结果表明文中给出的算法进一步弥补了傅立叶方法的不足,对于较短的基因序列,其预测精度高于现有的基因识别算法。
Gene is the basic unit of heredity and is DNA segment with genetic information, but non-gene can not code protein, so finding genes in DNA sequences has been an important problem in bioinformatics for a long time. In this dissertation, theory and methods of signal processing including transform domain, digital filter, time-frequency analysis, statistical learning and intelligent algorithms are applied to identify genes.
Firstly, theory of filter for gene prediction is analyzed, therefore two important elements are proposed: length and weak/strong periodicity of protein coding regions. According to the periodicity of the coding regions, the FIR filter and the adaptive filters with narrow pass-band are developed. The predicted locations of the exons are achieved by calculating the annotated gene sequence. The experimental results indicate that the designed filters are valid and can improve accuracy of gene identification.
Secondly, an improved Fourier transform approach is proposed by integrating the gene prediction filter with the Fourier transform. This algorithm can magnify period-3 signals, remove the background noises, and is not restricted by the length of the predicted sequences unlike the existing Fourier methods. The experimental results show that the improved Fourier method can promote predictive accuracy. At the same time, the Fourier method based on sliding window is applied to identify the coding and noncoding regions in DNA sequences.
Thirdly, the forward algorithm integrated with the Hidden Markov Model of coding regions is applied to predict exons in genes. By identifying the annotated gene sequence the designed algorithm is valid and reduces the computational complication. At the same time, the algorithm for gene classification based on support vector machine is schemed. The experimental results show that the proposed method can not only improve accuracy, but also reduce training data.
At last, four features and three discriminate analysis methods are studied for improving predictive accuracy, and the gene identification algorithm based on multiple features is proposed. The experimental results indicate that the developed algorithm can improve the Fourier methods and has better accuracy than the existing gene prediction method for short DNA sequences.
引文
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