转录因子结合位点识别问题的算法研究
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摘要
转录是基因表达的第一阶段，也是基因调节的主要阶段，通过转录因子与特异的DNA序列结合，对基因的表达起抑制或增强的作用。识别DNA序列的中的这些结合区域，即转录因子结合位点识别，对了解基因的转录活性及理解基因表达有着重要意义，是现今生物信息学中最为广泛研究的问题之一。
转录因子结合位点识别问题的难点在于，与大量长度几百或上千碱基的背景噪声序列相比，长度为十几或几十的模体信号相对较短，并且同一转录因子的模体实例还有可能部分发生变异。同时，随着序列长度和数量的增加，解空间大小也会飞速巨增，计算开销往往不切实际。此外，识别结合区域中的多个转录因子结合位点、寻找特定的共调控转录因子结合位点组合以及在全基因组范围内寻找结合位点，也是此问题所面临的巨大挑战。本论文针对转录因子结合位点识别问题中所使用的数学模型、优化技术、高效识别方法以及与新型生物实验结合的进一步发展等问题进行了深入的研究，将所提出的方法应用于模拟字符串数据、不同物种和组织的启动子序列和全基因组的DNA数据进行转录因子结合位点识别。主要工作可概括如下：
（1）针对传统转录因子结合位点识别问题组合候选解集规模过大，经典的概率求解方法易于陷入局部最优解的情况，提出了定位投影求精算法。通过一个基于位置频率矩阵的定位投影过程，将数据集划分，聚类为不同的子集。从这些子集中过滤筛选出具有一定信息量和复杂度若干子集，分别作为期望最大化算法的初始状态并进行迭代求精。本论文通过对定位投影过程中阀值的设定，实现了对OOPS、ZOOPS、TCM三种模体实例不同分布模型的处理。同时，结合高阶马尔可夫模型作为背景加强模体特异性，使概率模型更加符合真实生物数据。此外，引入了相似函数对各子集输出结果进行评估，使得定位投影求精算法可以解决多模体识别问题。实验结果表明，该算法可以在多个真核物种的启动子序列中有效识别转录因子结合位点。
（2）针对由转录因子结合位点识别问题衍生得到的(l, d)植入模体搜索问题，传统算法在效率和准确度上往往较难达到良好的平衡，并且难以解决挑战实例的情况，提出了一种基于期望最大化的启发式聚类算法CEM。通过参照序列的设定，该算法将数据集划分为不同的子集，并使用改进的期望最大化算法来探索子集中最好的局部最优解。CEM将精确方法与概率方法相结合，克服了传统期望最大化算法陷入不同局部解的缺点，可准确寻找到植入位点，对识别高退化性模体有较好的性能。模拟数据测试结果表明，CEM不但能准确识别一般实例中的植入模体信号，对于挑战实例的植入模体信号识别也有较高准确率。此外，真实数据实验证明该算法可有效应用于实际物种的转录因子结合位点识别问题。
（3）针对全基因组范围的转录因子结合位点识别问题，提出了一种用于ChIP-seq数据的转录因子结合位点识别算法MMFChIP。该算法将精确方法和概率方法相结合，针对ChIP-seq的数据特点，通过对正负两个输入集合的比较，选出发生频率较高且相似的子序列生成位置频率矩阵，并结合模体内位置依赖性和高阶马尔可夫进行统计建模，利用错误发现率对预测实例进行控制。在输出时，还利用一个后处理过程聚类相似的模体。ChIP-seq数据测试证明，MMFChIP适用于处理大规模数据中的模体发现问题，不但可以发现数据中的多个模体成分，并且对这些数据中的潜在辅助因子也可以进行较好的预测。
The first step of gene expression, transcription, is the main step of generegulation. Through transcription factors binding the specific-DNA sequences, geneexpression can be suppressed or enhanced. Identifying the binding regions of DNAsequences, that is, the identification of transcription factor binding sites, has the greatsignificance with understanding the transcriptional activity of genes andcomprehending gene expression, which is one of the most widely researches inbioinformatics now.
The difficults of transcription factor binding sites identification problem are thatthe motif signal with length more than tens is relatively short with comparing to alarge number of the background noise sequences with length hundreds or thousands ofbases, and the motif instances of the same transcription factor may also partiallymutate. Meanwhile, with the length and the number of sequences increasing, the sizeof solution space will increase rapidly, and the computational cost will becomeimpractical. In addition, identifying multiple transcription factor binding sites in thebinding region, finding the specific co-regulated transcription factor binding sites anddiscovering the genome-wide binding sites, are the great challenges faced by thisproblem. In this paper, our researches are aimed at the mathematical models,optimization techniques, efficient identification methods and the further developmentscombining with the new biological experiments. The proposed methods are applied tothe the string simulate data, the promoter sequences of different species and tissuesand genome-wide DNA data to identify transcription factor binding sites.
(1) We propose a novel fixed-position projection refinement algorithm in orderto solve the large candidate solution space of traditional transcription factorbinding sites identification problem and alleviate the classic methods to fallinto local optimal. This algorithm divides the data into different subsetsthrough a projection process based on the corresponding probabilisticfrequency matrix, it filters the subsets with certain information score andcomplexity score, which are used as the initial condition for expectationmaximum refinement. Our algorithm achieves the different motif instancesdistribution in the model OOPS, ZOOPS and TCM by setting the threshold inthe fixed-position projection process. Meanwhile, using the high orderMarkov model as background can make the probability model more closer to the reality. In addation, our algorithm can be extended to a multiple motifsdiscovery version by using the similarity function. Experimental results showthat the algorithm can effectively identify transcription factor binding sites inthe promoter sequences in multiple eukaryotic species.
(2) For the planted (l, d) motif search problem derived from the transcriptionfactor binding sites identification, traditional algorithms are hard to make thegoog trade-off between efficiency and accuracy, and also hard to solve thechallenging instances. We present a heuristic cluster based EM algorithm,CEM, which refines the cluster subsets in the modified EM method toexplore the best local optimal solution through setting the reference sequence.Combining the exact methods and the probabilistic methods, CEM overcomethe shortcomings of the traditional EM algorithm falling into different localsolutions. Our algorithm can accurately find the planted sites and has betterperformance on the high degenerated motifs. Simulation data experimentsshow that CEM can not only accurately identify the planted motif signals inthe practical instances but also has a better accuracy in the challenginginstances. In addition, the real data experiments show that the algorithm canbe effectively applied to real species for identifying the transcription factorbinding sites.
(3) For identifying the genome-wide transcription factor binding sites, wepropose an algorithm, MMFChip, to identify the binding motifs oftranscription factors in ChIP-seq data. Our algorithm is motivated on thecharacteristics that the amount and quality of ChIP-seq data have beendramatically increased. Through the word enumeration strategy of countingthe occurrences of the subsequences in the positive and negative set,MMFChip combines the exact methods and the probabilistic methods, whichselects the relative enriched subsequences and searches the (l, d) instances ofeach enriched subsequence to constitute the corresponding Position WeightMatrixes. Then MMFChip employs the statistic model including theintra-motif dependency and high-order Markov model to give the motif amore precise statistic description, and utilizes the False Discovery Rate tocontrol the quality of outputs. Finally, MMFChip uses a post-processing tocluster the similar motifs. Applying MMFChip on the ChIP-seq datasets, theresults show that our algorithm can process the motif discovery problem in the large-scale data, the mutiple binding motifs and co-factors can beeffectively detected.
转录因子结合位点识别问题的难点在于，与大量长度几百或上千碱基的背景噪声序列相比，长度为十几或几十的模体信号相对较短，并且同一转录因子的模体实例还有可能部分发生变异。同时，随着序列长度和数量的增加，解空间大小也会飞速巨增，计算开销往往不切实际。此外，识别结合区域中的多个转录因子结合位点、寻找特定的共调控转录因子结合位点组合以及在全基因组范围内寻找结合位点，也是此问题所面临的巨大挑战。本论文针对转录因子结合位点识别问题中所使用的数学模型、优化技术、高效识别方法以及与新型生物实验结合的进一步发展等问题进行了深入的研究，将所提出的方法应用于模拟字符串数据、不同物种和组织的启动子序列和全基因组的DNA数据进行转录因子结合位点识别。主要工作可概括如下：
（1）针对传统转录因子结合位点识别问题组合候选解集规模过大，经典的概率求解方法易于陷入局部最优解的情况，提出了定位投影求精算法。通过一个基于位置频率矩阵的定位投影过程，将数据集划分，聚类为不同的子集。从这些子集中过滤筛选出具有一定信息量和复杂度若干子集，分别作为期望最大化算法的初始状态并进行迭代求精。本论文通过对定位投影过程中阀值的设定，实现了对OOPS、ZOOPS、TCM三种模体实例不同分布模型的处理。同时，结合高阶马尔可夫模型作为背景加强模体特异性，使概率模型更加符合真实生物数据。此外，引入了相似函数对各子集输出结果进行评估，使得定位投影求精算法可以解决多模体识别问题。实验结果表明，该算法可以在多个真核物种的启动子序列中有效识别转录因子结合位点。
（2）针对由转录因子结合位点识别问题衍生得到的(l, d)植入模体搜索问题，传统算法在效率和准确度上往往较难达到良好的平衡，并且难以解决挑战实例的情况，提出了一种基于期望最大化的启发式聚类算法CEM。通过参照序列的设定，该算法将数据集划分为不同的子集，并使用改进的期望最大化算法来探索子集中最好的局部最优解。CEM将精确方法与概率方法相结合，克服了传统期望最大化算法陷入不同局部解的缺点，可准确寻找到植入位点，对识别高退化性模体有较好的性能。模拟数据测试结果表明，CEM不但能准确识别一般实例中的植入模体信号，对于挑战实例的植入模体信号识别也有较高准确率。此外，真实数据实验证明该算法可有效应用于实际物种的转录因子结合位点识别问题。
（3）针对全基因组范围的转录因子结合位点识别问题，提出了一种用于ChIP-seq数据的转录因子结合位点识别算法MMFChIP。该算法将精确方法和概率方法相结合，针对ChIP-seq的数据特点，通过对正负两个输入集合的比较，选出发生频率较高且相似的子序列生成位置频率矩阵，并结合模体内位置依赖性和高阶马尔可夫进行统计建模，利用错误发现率对预测实例进行控制。在输出时，还利用一个后处理过程聚类相似的模体。ChIP-seq数据测试证明，MMFChIP适用于处理大规模数据中的模体发现问题，不但可以发现数据中的多个模体成分，并且对这些数据中的潜在辅助因子也可以进行较好的预测。
The first step of gene expression, transcription, is the main step of generegulation. Through transcription factors binding the specific-DNA sequences, geneexpression can be suppressed or enhanced. Identifying the binding regions of DNAsequences, that is, the identification of transcription factor binding sites, has the greatsignificance with understanding the transcriptional activity of genes andcomprehending gene expression, which is one of the most widely researches inbioinformatics now.
The difficults of transcription factor binding sites identification problem are thatthe motif signal with length more than tens is relatively short with comparing to alarge number of the background noise sequences with length hundreds or thousands ofbases, and the motif instances of the same transcription factor may also partiallymutate. Meanwhile, with the length and the number of sequences increasing, the sizeof solution space will increase rapidly, and the computational cost will becomeimpractical. In addition, identifying multiple transcription factor binding sites in thebinding region, finding the specific co-regulated transcription factor binding sites anddiscovering the genome-wide binding sites, are the great challenges faced by thisproblem. In this paper, our researches are aimed at the mathematical models,optimization techniques, efficient identification methods and the further developmentscombining with the new biological experiments. The proposed methods are applied tothe the string simulate data, the promoter sequences of different species and tissuesand genome-wide DNA data to identify transcription factor binding sites.
(1) We propose a novel fixed-position projection refinement algorithm in orderto solve the large candidate solution space of traditional transcription factorbinding sites identification problem and alleviate the classic methods to fallinto local optimal. This algorithm divides the data into different subsetsthrough a projection process based on the corresponding probabilisticfrequency matrix, it filters the subsets with certain information score andcomplexity score, which are used as the initial condition for expectationmaximum refinement. Our algorithm achieves the different motif instancesdistribution in the model OOPS, ZOOPS and TCM by setting the threshold inthe fixed-position projection process. Meanwhile, using the high orderMarkov model as background can make the probability model more closer to the reality. In addation, our algorithm can be extended to a multiple motifsdiscovery version by using the similarity function. Experimental results showthat the algorithm can effectively identify transcription factor binding sites inthe promoter sequences in multiple eukaryotic species.
(2) For the planted (l, d) motif search problem derived from the transcriptionfactor binding sites identification, traditional algorithms are hard to make thegoog trade-off between efficiency and accuracy, and also hard to solve thechallenging instances. We present a heuristic cluster based EM algorithm,CEM, which refines the cluster subsets in the modified EM method toexplore the best local optimal solution through setting the reference sequence.Combining the exact methods and the probabilistic methods, CEM overcomethe shortcomings of the traditional EM algorithm falling into different localsolutions. Our algorithm can accurately find the planted sites and has betterperformance on the high degenerated motifs. Simulation data experimentsshow that CEM can not only accurately identify the planted motif signals inthe practical instances but also has a better accuracy in the challenginginstances. In addition, the real data experiments show that the algorithm canbe effectively applied to real species for identifying the transcription factorbinding sites.
(3) For identifying the genome-wide transcription factor binding sites, wepropose an algorithm, MMFChip, to identify the binding motifs oftranscription factors in ChIP-seq data. Our algorithm is motivated on thecharacteristics that the amount and quality of ChIP-seq data have beendramatically increased. Through the word enumeration strategy of countingthe occurrences of the subsequences in the positive and negative set,MMFChip combines the exact methods and the probabilistic methods, whichselects the relative enriched subsequences and searches the (l, d) instances ofeach enriched subsequence to constitute the corresponding Position WeightMatrixes. Then MMFChip employs the statistic model including theintra-motif dependency and high-order Markov model to give the motif amore precise statistic description, and utilizes the False Discovery Rate tocontrol the quality of outputs. Finally, MMFChip uses a post-processing tocluster the similar motifs. Applying MMFChip on the ChIP-seq datasets, theresults show that our algorithm can process the motif discovery problem in the large-scale data, the mutiple binding motifs and co-factors can beeffectively detected.
引文
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