Implementation of a Classification-Based Prediction Model for Plant mRNA Poly(A)Sites

The poly(A) site of a messenger RNA (mRNA) defines the end of a transcript during eukaryotic gene expression. Finding poly(A) sites in genome sequences can help to annotate the ends of genes and predict alternative polyadenylation. However, it is challenging to predict plant poly(A) sites using computational methods because of the weak signals that determine the poly(A) sites. Here we describe a classification based plant poly(A) site recognition model. First, several feature representation methods like factorial moments, M encoding, and weight of signal patterns are adopted to describe the makeup of nucleotide sequences of poly(A) signals. Then, a training and testing model using Bayesian network as the classification algorithm are built to predict plant poly(A) sites. Comparing to previous plant poly(A) sites prediction software PASS that we developed and three other models concerned about human or animal data, the recognition model introduced here shows good performance, flexibility and expansibility.