Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis.

ABSTRACT:

Background

Alternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3' end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes.

Results

Here we proposed a computational framework, named PASCCA, for

SUBMITTER: Ye W 

PROVIDER: S-EPMC6343338 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature