piRNA ping-pong analysis of complementary sequences
Usage
pingpong(bam_file)
Arguments
bam_file
An object of class data.frame or DataFrame
Details
The ping-pong mechanism is a proposed method for the amplification of primary piRNAs, which leads to the production of new primary piRNAs from their precursor transcripts, which eventually amplifies the pool of both primary and secondary piRNAs. This positive feedback loop is a secondary biogenesis mechanism that requires complementary transcripts to a pre-existing pool of piRNAs.
Value
This function returns a data.frame object with frequency of overlapping complementary piRNAs.
Author(s)
Diana H.P. Low
References
Brennecke J. et al. Cell 128, 1089-1103, March 23, 2007
Examples
data(ssviz)
pp<-pingpong(pctrlbam)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ssviz)
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: Biostrings
Loading required package: XVector
Loading required package: reshape
Attaching package: 'reshape'
The following objects are masked from 'package:S4Vectors':
expand, rename
Loading required package: ggplot2
Loading required package: RColorBrewer
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ssviz/pingpong.Rd_%03d_medium.png", width=480, height=480)
> ### Name: pingpong
> ### Title: pingpong
> ### Aliases: pingpong
>
> ### ** Examples
>
> data(ssviz)
> pp<-pingpong(pctrlbam)
No counts column provided in bam file. Running unique counts.
Otherwise provide a count matrix or run getCountMatrix.
Checking rnames:
L1_MM
L1_MM
***************
Unique forward reads: 299 , reverse reads: 201
Forward read # 1 out of 299
Forward read # 21 out of 299
Forward read # 41 out of 299
Forward read # 61 out of 299
Forward read # 81 out of 299
Forward read # 101 out of 299
Forward read # 121 out of 299
Forward read # 141 out of 299
Forward read # 161 out of 299
Forward read # 181 out of 299
Forward read # 201 out of 299
Forward read # 221 out of 299
Forward read # 241 out of 299
Forward read # 261 out of 299
Forward read # 281 out of 299
Done.
>
>
>
>
>
> dev.off()
null device
1
>