Introduction to MAAPER

Wei Vivian Li, Rutgers Department of Biostatistics and Epidemiology


MAAPER is a computational method for model-based analysis of alternative polyadenylation using 3’ end-linked reads. It uses a probabilistic model to predict polydenylation sites (PASs) for nearSite reads with high accuracy and sensitivity, and examines different types of alternative polyadenylation (APA) events, including those in 3’UTRs and introns, using carefully designed statistics.

maaper requires three input files:

The final output of mapper are two text files named “gene.txt” and “pas.txt”, which contain the predicted PASs and APA results.

Below is a basic example which shows how to use the maaper function. The bam and gtf files used in this example can be downloaded here. To save computation time, we are providing a toy example dataset of chr19. In real data application, we do not recommend dividing the files into subsets by chromosomes.


pas_annotation = readRDS("./mouse.PAS.mm9.rds")
gtf = "./gencode.mm9.chr19.gtf"
# bam file of condition 1 (could be a vector if there are multiple samples)
bam_c1 = "./NT_chr19_example.bam"
# bam file of condition 2 (could be a vector if there are multiple samples)
bam_c2 = "./AS_4h_chr19_example.bam"

maaper(gtf, # full path of the GTF file
       pas_annotation, # PAS annotation
       output_dir = "./", # output directory
       bam_c1, bam_c2, # full path of the BAM files
       read_len = 76, # read length
       ncores = 12  # number of cores used for parallel computation 

Please note the following options in the mapper function: