Binding matrix: a novel approach for binding site recognition.
Binding matrix: a novel approach for binding site recognition.
复制标题
DOI：
10.1142/s0219720004000569
复制
发表时间：
2004-06-01
影响因子：
1
通讯作者：
Martinetz, Thomas
中科院分区：
文献类型：
--
作者：
Kim, Jan T;Gewehr, Jan E;Martinetz, Thomas
识别基因组序列中的蛋白质- dna结合位点是发现基因组序列生物学功能的关键步骤。序列信息的爆炸性增长导致了对高特异性结合位点检测方法的需求。这里提出的工作的动机是通过基于最大似然估计的系统方法来解决这一需求。开发了一个通用框架，在该框架中可以以统一和一致的方式描述大类结合位点检测方法。蛋白质与dna的结合是由结合能决定的，结合能是序列词空间内的近似线性函数。所有基于矩阵的绑定词检测器都可以看作是不同的线性分类器，它们试图估计由结合能函数隐含的线性分离。在此框架下描述了一致性序列和轮廓矩阵的标准方法。确定这种线性分离的最大似然方法导致一种新的矩阵类型，称为结合矩阵。绑定矩阵是最具体的基于矩阵的分类器，它与已知绑定词的输入集一致。与其他基质相比，它在特异性上有了显著的提高。这是用TRANSFAC数据库提供的95组实验确定的结合词来证明的。
Recognition of protein-DNA binding sites in genomic sequences is a crucial step for discovering biological functions of genomic sequences. Explosive growth in availability of sequence information has resulted in a demand for binding site detection methods with high specificity. The motivation of the work presented here is to address this demand by a systematic approach based on Maximum Likelihood Estimation. A general framework is developed in which a large class of binding site detection methods can be described in a uniform and consistent way. Protein-DNA binding is determined by binding energy, which is an approximately linear function within the space of sequence words. All matrix based binding word detectors can be regarded as different linear classifiers which attempt to estimate the linear separation implied by the binding energy function. The standard approaches of consensus sequences and profile matrices are described using this framework. A maximum likelihood approach for determining this linear separation leads to a novel matrix type, called the binding matrix. The binding matrix is the most specific matrix based classifier which is consistent with the input set of known binding words. It achieves significant improvements in specificity compared to other matrices. This is demonstrated using 95 sets of experimentally determined binding words provided by the TRANSFAC database.