# the RESPECT R-package has two main functions:
# 1. exome-based peak
# 2. compute reads counts, rpkm and fold change
# please feel free to contact liulian19860905@163.com for any questions
# load library and specify the parameters
gene_anno_gtf <- system.file("extdata", "example.gtf", package="exomePeak")
f1 <- system.file("extdata", "IP1.bam", package="exomePeak")
f2 <- system.file("extdata", "IP2.bam", package="exomePeak")
f3 <- system.file("extdata", "IP3.bam", package="exomePeak")
f4 <- system.file("extdata", "IP4.bam", package="exomePeak")
ip_bam <- c(f1,f2,f3,f4)
f1 <- system.file("extdata", "Input1.bam", package="exomePeak")
f2 <- system.file("extdata", "Input2.bam", package="exomePeak")
f3 <- system.file("extdata", "Input3.bam", package="exomePeak")
input_bam <- c(f1,f2,f3)
input2bam <- list(4)
input2bam[[1]] <- c(1,2) # the 1st ip sample uses the 1st & 2nd input samples as control
input2bam[[2]] <- c(1,2) # the 2nd ip sample uses the 1st & 2nd input samples as control
input2bam[[3]] <- c(1,2) # the 3rd ip sample uses the 1st & 2nd input samples as control
input2bam[[4]] <- c(3) # the 4th ip sample uses the 3rd input sample as control
# Extract the combinatorial RNA methylome
# unfortunately, this function has not been optimized for parallel processing.
# This part will take a really long time on real data
RMT(INPUT_BAM=input_bam, IP_BAM=ip_bam, INPUT2IP=input2bam, GENE_ANNO_GTF=gene_anno_gtf)
# RMT result will be generated in RMT_result folder under current working directoy, including:
# the merged peaks or the combinatorial RNA methylome from all replicates
# the bed file, which can be viewed under IGV
# reads count of all peaks for both the ip and input samples
# RPKM of all the peaks for both the ip and input samples (for your reference only).
# Alternatively, exomePeak package can automatically download the complete transcriptome.
# And then scan the entire transcriptome for RNA methylation sites
# It will take a long time
# RMT(INPUT_BAM=input_bam, IP_BAM=ip_bam, INPUT2IP=input2bam, GENOME="hg19")
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