Recognition of polyadenylation sites from Arabidopsis genomic sequences.
A polyadenine tail is found at the 3' end of nearly every fully processed eukaryotic mRNA and has been suggested to influence virtually all aspects of mRNA metabolism. The ability to predict polyadenylation site will allow us to define gene boundaries, predict number of genes present in a particular gene locus and perhaps better understand mRNA metabolism. To this end, we built an arabidopsis polyadenylation prediction model. The prediction model uses a machine learning method which consists of four sequential steps: feature generation, feature selection, feature integration and cascade classifier. We have tested our model on public datasets and achieved more than 97% sensitivity and specificity. We have also directly compared with another arabidopsis prediction model, PASS 1.0, and have achieved better results.
School of Computing, National University of Singapore, COM1, Law Link, Singapore 117590. kohchuan@comp.nus.edu.sg
Chuan Hock
CH
Koh
Limsoon
L
Wong
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics*; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D017343:Genes, Plant; D018745:Genome, Plant; D008954:Models, Biological; D008957:Models, Genetic; D010940:Plant Proteins / Q000737:chemistry; D026723:Polyadenylation; D012333:RNA, Messenger / Q000378:metabolism; D012680:Sensitivity and Specificity; D012984:Software
D010940:Plant Proteins; D012333:RNA, Messenger