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Recognition of human promoter based on GMM and Rough Set

Recognition of Human Promoter based on GMM and Rough Set

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第36届中国控制会议

作者： GUO Shuo YUAN De-cheng Guo Wa Zhang Bochen Li Jin-na Information Engineering College Shenyang University of Chemical Technology State Grid Liaoning Electric Power Supply Co. Ltd Tieling Power Supply Company East China University of Science and Technology

ISBN: (纸本)9781538629185

Identifying human promoter is the foundation for understanding gene regulation and the key point of large-scale function prediction. Although many algorithms have been proposed, they are rather complex with the performance still limited by low sensitivity and high false positives. In this paper, Gaussian Mixture Model is used to build the model of positional densities of oligonucleotides as the promoter features. Rough Set is applied for analysis the relationship of the binding sites occurence at same time and identify the human promoter sequence. Clustering algorithm which using the inverse of fuzzy likelihood function as the cluster distance is used to estimate the Gaussian Mixture Model optimal number and the parameters of sub-models. And Least Square is used to calculate the mixing proportions. The optimal solution and efficient convergence are obtained. The features for building the predict model are not perfect, so Rough Set Theory is applied for building the promoter information table finding the relationship of the motifs at upstream and downstream of promoter sequence, at the mean time, to predict human promoter sequence. The simulation results show the accuracy of the prediction model is high.

关键词： human promoter Gaussian Mixture Model fuzzy likelihood function cluster Rough Set

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Sequence-dependent geometric flexibility of core promoter regions in human genome

Sequence-dependent geometric flexibility of core promoter re...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Yongchun Zuo Yong Peng The National Research Center for Animal Transgenic Biotechnology Inner Mongolia University School of Physical Science and Technology Inner Mongolia University

Background:The general framework of promoter mechanism is dependent on protein- DNA interaction in which the different transcription factors(TFs) bind on the promoter region to enable its *** many studies have been taken on this area,the mechanism of transcription regular is still *** it is necessary to investigate promoter sequences in higher dimensions than the sequence alone. Dataset and Methods:The 4263 CpG related and 1683 non-CpG related human promoters were downloaded from the UCSC Genome Browser(http://***/). The sequences with 400 lbp length from 2000bp upstream to 2000bp downstream of the transcription start sites(TSSs the 0th sites) were selected as the core *** on the physical potentials derived from quantum chemical calculations of atomic molecular dynamics (MD) simulations,In this study we introduced physical structural variables by defining three displacements(rise,shift and slide) and three rotation angles(twist,roll and tilt) for each dinucleotide to describe transcription initiation of human promoter. Results:Using 16 experimental parameters of nearest-neighbor dinucleotides based on the structural models,we converted the core-promoter sequence into a string of numerical values for studying the structural *** analysis results reveal that there are significant difference of structural patterns between the CpG related and non-CpG related promoter region,which indicated that the thermodynamic mechanism of transcription initiation for CpG related and non-CpG gene may be *** clearly observed that the structure profiles of CpG related promoter have the enhanced stiffness or flexibility than the non-CpG related promoter,particular in the shift profile and the twist *** believe that the six flexibility parameters will be helpful for further elucidating the transcription initiation regulation and improving the identification accuracy of human promoter.

关键词： human promoter DNA structural flexibility Transcription initiation regulation promoter prediction

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