Genome level analysis of rice mRNA 3'-end processing signals and alternative polyadenylation.

The position of a poly(A) site of eukaryotic mRNA is determined by sequence signals in pre-mRNA and a group of polyadenylation factors. To reveal rice poly(A) signals at a genome level, we constructed a dataset of 55 742 authenticated poly(A) sites and characterized the poly(A) signals. This resulted in identifying the typical tripartite cis-elements, including FUE, NUE and CE, as previously observed in Arabidopsis. The average size of the 3'-UTR was 289 nucleotides. When mapped to the genome, however, 15% of these poly(A) sites were found to be located in the currently annotated intergenic regions. Moreover, an extensive alternative polyadenylation profile was evident where 50% of the genes analyzed had more than one unique poly(A) site (excluding microheterogeneity sites), and 13% had four or more poly(A) sites. About 4% of the analyzed genes possessed alternative poly(A) sites at their introns, 5'-UTRs, or protein coding regions. The authenticity of these alternative poly(A) sites was partially confirmed using MPSS data. Analysis of nucleotide profile and signal patterns indicated that there may be a different set of poly(A) signals for those poly(A) sites found in the coding regions. Based on the features of rice poly(A) signals, an updated algorithm termed PASS-Rice was designed to predict poly(A) sites.

Predictive modeling of plant messenger RNA polyadenylation sites.

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. CONCLUSION: 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.