title: ProMT: effective human promoter prediction using Markov chain model based on DNA structural properties.
abstract: The core promoters play significant and extensive roles for the initiation and regulation of DNA transcription. The identification of core promoters is one of the most challenging problems yet. Due to the diverse nature of core promoters, the results obtained through existing computational approaches are not satisfactory. None of them considered the potential influence on performance of predictive approach resulted by the interference between neighboring TSSs in TSS clusters. In this paper, we sufficiently considered this main factor and proposed an approach to locate potential TSS clusters according to the correlation of regional profiles of DNA and TSS clusters. On this basis, we further presented a novel computational approach (ProMT) for promoter prediction using Markov chain model and predictive TSS clusters based on structural properties of DNA. Extensive experiments demonstrated that ProMT can significantly improve the predictive performance. Therefore, considering interference between neighboring TSSs is essential for a wider range of promoter prediction.
authors.forename: Dapeng
authors.initials: D
authors.lastname: Xiong
authors.forename: Rongjie
authors.initials: R
authors.lastname: Liu
authors.forename: Fen
authors.initials: F
authors.lastname: Xiao
authors.forename: Xieping
authors.initials: X
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D005810:Multigene Family / Q000235:genetics; D011401:Promoter Regions, Genetic / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D004247:DNA