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A combined statistical model for multiple motifs search

Chinese Physics B 2008年第12期17卷 4396-4400页

作者：高丽锋刘鑫官山 Chinese Academy of Agriculture Science Institute of Theoretical Physics Physics Science and Technology Department Yangzhou University

Transcription factor binding sites （TFBS） play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with definite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite different from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for finding over- represented short sequence segments in different kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in ***, PlantProm DB which is a non-redundant collection of proximal promoter sequences from different species, collection of the intergenic sequences of the whole genome of ***. Even though the complexity of these three data sets is quite different, the results show that this model is rather general and sensible.

关键词： transcription factor binding sites motif position weight matrix

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Improving cis-regulatory elements modeling by consensus scaffolded mixture models

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Science China(Information Sciences) 2013年第1期56卷 219-229页

作者： JIANG HongShan ZHAO Ying CHEN WenGuang ZHANG XueGong Department of Computer Science and Technology Tsinghua University MOE Key Laboratory of Bioinformatics and the Bioinformatics Division Department of AutomationTsinghua University

A position weight matrix(PWM) is widely accepted as a probabilistic representation for modeling protein-DNA binding *** studies showed that for factors which bind to divergent binding sites,mixtures of multiple PWMs improve *** propose a consensus scaffolded mixutre PWM(CSM) model to improve cis-regulatory elements modeling by allowing overlapping components represented by a set of PWMs,each of which corresponds to a binding pattern and is scaffolded by a degenerate *** addition,we propose a learning algorithm that involves an initial structure learning stage based on the frequent pattern mining and a refining stage based on the expectation maximization(EM) *** assess the merits of CSM using three independent *** a case-study of transcription factor Leu3,the derived CSM models agree with conventional mixtures but show better fitness according to Fermi-Dirac *** of the human-mouse conservation of predicted binding sites of 83 JASPAR transcription factors(TFs) shows that the CSM is as good as or better than the simple mixture,the context-specific independent(CSI) mixture,and the single PWM model,for 83%,84%,and 75% of the cases,***-fold cross validation on 46 TRANSFAC datasets shows that CSM model has better generality than other mixture models.

关键词： position weight matrix cis-regulatory elements mixture models frequent pattern mining EM algorithm

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Eukaryotic Promoter Recognition Using Back propagation Neural Network

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Chinese Journal of Biomedical Engineering(English Edition) 2004年第2期13卷 87-92页

作者： XIONGQing WANGYuan-Qiang LIZhi-Liang CollegeofChemistryandChemicalEngineering CenterforBiochemistryandPharmaceuticalEngineering KeyLaboratoryofBiomedicalEngineeringofEducationalMinistryandChongqingCity

A new system is developed to recognize promoter sequences from non promoter sequences based on position weight matrix and backpropagation neural network in this paper. The system performs significantly better on the training set and the test set, the mean recognition rate is as high as 99% on the training set and 97% on the testing set. Experimental results demonstrate the effectiveness of the system to recognize the promoter sequences that have been trained and the promoter sequences that have not been seen previously.

关键词： Eukaryotic promoter recognition BP neural network position weight matrix

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