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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 mapper
function:
maaper
users the unpaired test. Please set paired = TRUE
in 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 FALSE
by default.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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