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Version 1.0.2-3: move to conda-based dependency resolution.
author pjbriggs
date Wed, 28 Feb 2018 10:22:32 -0500
parents f411ce97a351
children
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/home/pjb/galaxy-tools/ceas/test.tool_dependencies.ceas/bx_python/0.7.1/lib/python2.7/site-packages/pkg_resources.py:1054: UserWarning: /home/pjb/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).
  warnings.warn(msg, UserWarning)
ceasBW -- 0.9.9.7 (package version 1.0.2)
/home/pjb/galaxy-tools/ceas/test.tool_dependencies.ceas/bx_python/0.7.1/lib/python2.7/site-packages/pkg_resources.py:1054: UserWarning: /home/pjb/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).
  warnings.warn(msg, UserWarning)
INFO  @ Tue, 23 Jun 2015 13:03:32: 
# ARGUMENTS: 
# name = ceas
# gene annotation table = galGal3.refGene
# BED file = ceas_in.bed
# WIG file = ceas_in.bigwig
# extra BED file = None
# ChIP annotation = On
# gene-centered annotation =  On
# average profiling = On
# dump profiles = Off
# re-annotation for genome background (ChIP region annotation) = False
# promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
# downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
# bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
# span size (gene-centered annotation) = 3000 bp
# profiling resolution (average profiling) = 50 bp
# relative distance wrt TSS and TTS (average profiling) = 3000 bp 
INFO  @ Tue, 23 Jun 2015 13:03:32: #1 read the gene table... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #2 read the bed file of ChIP regions... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #3 perform gene-centered annotation... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #4 See ceas.xls for gene-centered annotation! 
INFO  @ Tue, 23 Jun 2015 13:03:32: #5 read the pre-computed genome bg annotation... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #6 perform ChIP region annotation... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #7 write a R script of ChIP region annotation... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #8-1 run wig profiling of chr26... 
INFO  @ Tue, 23 Jun 2015 13:03:32: #9 append an R script of wig profiling... 

R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-redhat-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.

  Natural language support but running in an English locale

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.

> # ARGUMENTS: 
> # name = ceas
> # gene annotation table = galGal3.refGene
> # BED file = ceas_in.bed
> # WIG file = ceas_in.bigwig
> # extra BED file = None
> # ChIP annotation = On
> # gene-centered annotation =  On
> # average profiling = On
> # dump profiles = Off
> # re-annotation for genome background (ChIP region annotation) = False
> # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
> # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
> # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
> # span size (gene-centered annotation) = 3000 bp
> # profiling resolution (average profiling) = 50 bp
> # relative distance wrt TSS and TTS (average profiling) = 3000 bp
> pdf("ceas.pdf",height=11.5,width=8.5)
> 
> # 13:03:32 Tue, 23 Jun 2015
> # 
> # ChIP annotation
> # 
> 
> 
> # 
> # Chromosomal Distribution
> # 
> 
> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> r0<-c(100.0)
> r1<-c(100.0)
> height<-rbind(r0,r1)
> names=c("26")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=TRUE,col=c("#5FA1C1","#EB9D86"),main="Chromosomal Distribution of ChIP Regions",xlab="Percentage %",ylab="Chromosome",border=FALSE,xlim=c(0.000000,183.333333),cex.names=1)
> text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9)
> text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9)
> legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> 
> # 
> # Promoter,Bipromoter,Downstream, Gene and Regions of interest
> # 
> 
> par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416)
> r1<-c(0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=1000 bp","<=2000 bp","<=3000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.757585),cex.names=1)
> text(x=mp[1,],y=c(1.8532425688606797, 3.616851183410451, 5.322318854623416),label=c("1.9 %","3.6 %","5.3 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
+ (0.981)","0.000 %
+ (0.964)","0.000 %
+ (0.947)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(0.03876062889120376, 0.03876062889120376)
> r1<-c(0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=2500 bp","<=5000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Bidirectional Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,0.071061),cex.names=1)
> text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 %
+ (1.000)","0.000 %
+ (1.000)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683)
> r1<-c(0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=1000 bp","<=2000 bp","<=3000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Downstream",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.131358),cex.names=1)
> text(x=mp[1,],y=c(1.8290171758036773, 3.4690762857627364, 4.980740812519683),label=c("1.8 %","3.5 %","5.0 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
+ (0.982)","0.000 %
+ (0.965)","0.000 %
+ (0.950)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054)
> r1<-c(0.0, 0.0, 0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("5'UTR","3'UTR","Coding Exon","Intron","All")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Gene",ylab="Percentage %",border=FALSE,ylim=c(0.000000,43.440571),cex.names=1)
> text(x=mp[1,],y=c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054),label=c("0.2 %","1.4 %","2.4 %","19.7 %","23.7 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 %
+ (0.998)","0.000 %
+ (0.986)","0.000 %
+ (0.976)","0.000 %
+ (0.803)","0.000 %
+ (0.763)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> 
> # 
> # Distribution of Genome and ChIP regions over cis-regulatory element
> # Note that the x may be modified for better graphics in case a value is too small
> # Thus, look at the labels of the pie chart to get the real percentage values
> # 
> 
> par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2))
> x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292)
> pie(x=x,labels=c("1.9 %","1.7 %","1.6 %","1.8 %","1.5 %","1.4 %","0.2 %","1.4 %","2.3 %","19.3 %","67.0 %"),main="Genome",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100)
> x<-c(0.000000,1.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
> legend("top",legend=c("Promoter (<=1000 bp): 1.9 %","Promoter (1000-2000 bp): 1.7 %","Promoter (2000-3000 bp): 1.6 %","Downstream (<=1000 bp): 1.8 %","Downstream (1000-2000 bp): 1.5 %","Downstream (2000-3000 bp): 1.4 %","5'UTR: 0.2 %","3'UTR: 1.4 %","Coding exon: 2.3 %","Intron: 19.3 %","Distal intergenic: 67.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n")
> x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000)
> pie(x=x,labels=c("0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","100.0 %"),main="ChIP",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100)
> x<-c(0.000000,1.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
> legend("top",legend=c("Promoter (<=1000 bp): 0.000 %","Promoter (1000-2000 bp): 0.000 %","Promoter (2000-3000 bp): 0.000 %","Downstream (<=1000 bp): 0.000 %","Downstream (1000-2000 bp): 0.000 %","Downstream (2000-3000 bp): 0.000 %","5'UTR: 0.000 %","3'UTR: 0.000 %","Coding exon: 0.000 %","Intron: 0.000 %","Distal intergenic: 100.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n")
> 
> # 
> # ChIP regions over the genome
> # 
> 
> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5))
> x<-c(0.000000,0.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,0.000000),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9)
> x<-c(0.000000,0.000000,0.000000,0.000000)
> y<-c(0.000000,0.000000,1.000000,1.000000)
> polygon(x,y,col=c("black"))
> x <- c(0.000000)
> y<-c(0.800000)
> lines(x, y,xlim=c(0, 0.0),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2)
> x<-c(0.000000,4127518.000000)
> y<-c(0.855556,1.144444)
> plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(0.000000,4127518.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n")
> start <- c(4119129)
> end <- c(4119130)
> vals <- c(0.0)
> vals[vals > 0.0] <- 0.0
> vals[vals < 0] <- 0
> heights <- 0.288889 * ((vals - 0)/(0.0 - 0)) + 0.855555555556
> for (i in 1:length(heights)) {
+ 	polygon(x=c(start[i], end[i], end[i], start[i]), y=c(0.855555555556, 0.855555555556, heights[i], heights[i]), col=c("#CC0000"), border=c("#CC0000"))
+ }
> mtext("26",side=2,line=0,outer=FALSE,at=1.0)
> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> layout(matrix(c(1, 2, 3, 3, 4, 5), 3, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 1, 1))
> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000)
> plot(x, y,type="l",main="Average Profile near TSS",xlab="Relative Distance to TSS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2)
> abline(v=0.000000,lty=2,col=c("black"))
> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000)
> plot(x, y,type="l",main="Average Profile near TTS",xlab="Relative Distance to TTS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2)
> abline(v=0.000000,lty=2,col=c("black"))
> x<-c(-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000,3050.000000,3100.000000,3150.000000,3200.000000,3250.000000,3300.000000,3350.000000,3400.000000,3450.000000,3500.000000,3550.000000,3600.000000,3650.000000,3700.000000,3750.000000,3800.000000,3850.000000,3900.000000,3950.000000,4000.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000)
> plot(x, y,type="l",main="Average Gene Profile",xlab="Upstream (bp), 3000 bp of Meta-gene, Downstream (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2)
> abline(v=0.000000,lty=2,col=c("black"))
> abline(v=3000.000000,lty=2,col=c("black"))
> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000)
> plot(x, y,type="l",main="Average Concatenated Exon Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,20000.000000),xaxt="s",yaxt="s",lwd=2)
> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000)
> plot(x, y,type="l",main="Average Concatenated Intron Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,20000.000000),xaxt="s",yaxt="s",lwd=2)
> par(mfrow=c(3, 2),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> x<-c(-3.000000,3.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s")
> x<-c(-3.000000,3.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s")
> x<-c(-3.000000,3.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s")
> x<-c(-3.000000,3.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s")
> x<-c(-3.000000,3.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s")
> x<-c(0.000000,4.761905,9.523810,14.285714,19.047619,23.809524,28.571429,33.333333,38.095238,42.857143,47.619048,52.380952,57.142857,61.904762,66.666667,71.428571,76.190476,80.952381,85.714286,90.476190,95.238095,100.000000)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000)
> plot(x, y,type="l",main="Average Intron Profile
+ (685 <= length < 2653 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,24000.000000),xaxt="s",yaxt="s",lwd=2)
> dev.off()
null device 
          1 
> 
INFO  @ Tue, 23 Jun 2015 13:03:33: #... cong! See ceas.pdf for the graphical results of CEAS!