title: Predictive modeling of plant messenger RNA polyadenylation sites.
abstract: BACKGROUND
  One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem.
  
  
  RESULTS
  Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called poly(A) site sleuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences.
  
  
  CONCLUSIONS
  Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites.
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005, PR China. glji@xmu.edu.cn
authors.forename: Guoli
authors.initials: G
authors.lastname: Ji
authors.forename: Jianti
authors.initials: J
authors.lastname: Zheng
authors.forename: Yingjia
authors.initials: Y
authors.lastname: Shen
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Wu
authors.forename: Ronghan
authors.initials: R
authors.lastname: Jiang
authors.forename: Yun
authors.initials: Y
authors.lastname: Lin
authors.forename: Johnny C
authors.initials: JC
authors.lastname: Loke
authors.forename: Kimberly M
authors.initials: KM
authors.lastname: Davis
authors.forename: Greg J
authors.initials: GJ
authors.lastname: Reese
authors.forename: Qingshun Quinn
authors.initials: QQ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D003198:Computer Simulation; D012323:RNA Processing, Post-Transcriptional; D012333:RNA, Messenger / Q000378:metabolism*; D018749:RNA, Plant / Q000378:metabolism*; D012680:Sensitivity and Specificity; D039221:mRNA Cleavage and Polyadenylation Factors / Q000378:metabolism
chemical_list: D012333:RNA, Messenger; D018749:RNA, Plant; D039221:mRNA Cleavage and Polyadenylation Factors