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.
library(MAAPER) = readRDS("./mouse.PAS.mm9.rds") pas_annotation = "./gencode.mm9.chr19.gtf" gtf # bam file of condition 1 (could be a vector if there are multiple samples) = "./NT_chr19_example.bam" bam_c1 # bam file of condition 2 (could be a vector if there are multiple samples) = "./AS_4h_chr19_example.bam" bam_c2 maaper(gtf, # full path of the GTF file # PAS annotation pas_annotation, output_dir = "./", # output directory # full path of the BAM files bam_c1, bam_c2, read_len = 76, # read length ncores = 12 # number of cores used for parallel computation )
Please note the following options in the
maaperusers the unpaired test. Please set
paired = TRUEin order to use the paired test. We recommend only using the paired test when samples are paired and sample size is relatively large.
bed = TRUE. It is set to