Alternative polyadenylation (APA) has been increasingly recognized as a crucial mechanism that contributes to transcriptome diversity and gene expression regulation. As RNA-seq has become a routine protocol for transcriptome analysis, it is of great interest to leverage such unprecedented collection of RNA-seq data by new computational methods to extract and quantify APA dynamics in these transcriptomes. However, research progress in this area has been relatively limited. Conventional methods rely on either transcript assembly to determine transcript 3 ends or annotated poly(A) sites. Moreover, they can neither identify more than two poly(A) sites in a gene nor detect dynamic APA site usage considering more than two poly(A) sites.