Mercurial > repos > pjbriggs > ceas

comparison test-data/ceas_out3.log @ 0:f411ce97a351 draft

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Uploaded initial version 1.0.2-2
author pjbriggs
date Tue, 30 Jun 2015 07:08:05 -0400
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-1:000000000000 0:f411ce97a351
1 /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).
2 warnings.warn(msg, UserWarning)
3 ceasBW -- 0.9.9.7 (package version 1.0.2)
4 /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).
5 warnings.warn(msg, UserWarning)
6 INFO @ Tue, 23 Jun 2015 13:03:32:
7 # ARGUMENTS:
8 # name = ceas
9 # gene annotation table = galGal3.refGene
10 # BED file = ceas_in.bed
11 # WIG file = ceas_in.bigwig
12 # extra BED file = None
13 # ChIP annotation = On
14 # gene-centered annotation = On
15 # average profiling = On
16 # dump profiles = Off
17 # re-annotation for genome background (ChIP region annotation) = False
18 # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
19 # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
20 # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
21 # span size (gene-centered annotation) = 3000 bp
22 # profiling resolution (average profiling) = 50 bp
23 # relative distance wrt TSS and TTS (average profiling) = 3000 bp
24 INFO @ Tue, 23 Jun 2015 13:03:32: #1 read the gene table...
25 INFO @ Tue, 23 Jun 2015 13:03:32: #2 read the bed file of ChIP regions...
26 INFO @ Tue, 23 Jun 2015 13:03:32: #3 perform gene-centered annotation...
27 INFO @ Tue, 23 Jun 2015 13:03:32: #4 See ceas.xls for gene-centered annotation!
28 INFO @ Tue, 23 Jun 2015 13:03:32: #5 read the pre-computed genome bg annotation...
29 INFO @ Tue, 23 Jun 2015 13:03:32: #6 perform ChIP region annotation...
30 INFO @ Tue, 23 Jun 2015 13:03:32: #7 write a R script of ChIP region annotation...
31 INFO @ Tue, 23 Jun 2015 13:03:32: #8-1 run wig profiling of chr26...
32 INFO @ Tue, 23 Jun 2015 13:03:32: #9 append an R script of wig profiling...
33
34 R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
35 Copyright (C) 2014 The R Foundation for Statistical Computing
36 Platform: x86_64-redhat-linux-gnu (64-bit)
37
38 R is free software and comes with ABSOLUTELY NO WARRANTY.
39 You are welcome to redistribute it under certain conditions.
40 Type 'license()' or 'licence()' for distribution details.
41
42 Natural language support but running in an English locale
43
44 R is a collaborative project with many contributors.
45 Type 'contributors()' for more information and
46 'citation()' on how to cite R or R packages in publications.
47
48 Type 'demo()' for some demos, 'help()' for on-line help, or
49 'help.start()' for an HTML browser interface to help.
50 Type 'q()' to quit R.
51
52 > # ARGUMENTS:
53 > # name = ceas
54 > # gene annotation table = galGal3.refGene
55 > # BED file = ceas_in.bed
56 > # WIG file = ceas_in.bigwig
57 > # extra BED file = None
58 > # ChIP annotation = On
59 > # gene-centered annotation = On
60 > # average profiling = On
61 > # dump profiles = Off
62 > # re-annotation for genome background (ChIP region annotation) = False
63 > # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
64 > # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
65 > # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
66 > # span size (gene-centered annotation) = 3000 bp
67 > # profiling resolution (average profiling) = 50 bp
68 > # relative distance wrt TSS and TTS (average profiling) = 3000 bp
69 > pdf("ceas.pdf",height=11.5,width=8.5)
70 >
71 > # 13:03:32 Tue, 23 Jun 2015
72 > #
73 > # ChIP annotation
74 > #
75 >
76 >
77 > #
78 > # Chromosomal Distribution
79 > #
80 >
81 > par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
82 > r0<-c(100.0)
83 > r1<-c(100.0)
84 > height<-rbind(r0,r1)
85 > names=c("26")
86 > 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)
87 > text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9)
88 > text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9)
89 > legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
90 >
91 > #
92 > # Promoter,Bipromoter,Downstream, Gene and Regions of interest
93 > #
94 >
95 > par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
96 > r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416)
97 > r1<-c(0.0, 0.0, 0.0)
98 > height<-rbind(r0,r1)
99 > names=c("<=1000 bp","<=2000 bp","<=3000 bp")
100 > 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)
101 > 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)
102 > text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
103 + (0.981)","0.000 %
104 + (0.964)","0.000 %
105 + (0.947)"),pos=3,offset=0.2)
106 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
107 > r0<-c(0.03876062889120376, 0.03876062889120376)
108 > r1<-c(0.0, 0.0)
109 > height<-rbind(r0,r1)
110 > names=c("<=2500 bp","<=5000 bp")
111 > 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)
112 > text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2)
113 > text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 %
114 + (1.000)","0.000 %
115 + (1.000)"),pos=3,offset=0.2)
116 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
117 > r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683)
118 > r1<-c(0.0, 0.0, 0.0)
119 > height<-rbind(r0,r1)
120 > names=c("<=1000 bp","<=2000 bp","<=3000 bp")
121 > 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)
122 > 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)
123 > text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
124 + (0.982)","0.000 %
125 + (0.965)","0.000 %
126 + (0.950)"),pos=3,offset=0.2)
127 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
128 > r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054)
129 > r1<-c(0.0, 0.0, 0.0, 0.0, 0.0)
130 > height<-rbind(r0,r1)
131 > names=c("5'UTR","3'UTR","Coding Exon","Intron","All")
132 > 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)
133 > 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)
134 > text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 %
135 + (0.998)","0.000 %
136 + (0.986)","0.000 %
137 + (0.976)","0.000 %
138 + (0.803)","0.000 %
139 + (0.763)"),pos=3,offset=0.2)
140 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
141 >
142 > #
143 > # Distribution of Genome and ChIP regions over cis-regulatory element
144 > # Note that the x may be modified for better graphics in case a value is too small
145 > # Thus, look at the labels of the pie chart to get the real percentage values
146 > #
147 >
148 > par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2))
149 > x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292)
150 > 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)
151 > x<-c(0.000000,1.000000)
152 > y<-c(0.000000,1.000000)
153 > plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
154 > 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")
155 > x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000)
156 > 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)
157 > x<-c(0.000000,1.000000)
158 > y<-c(0.000000,1.000000)
159 > plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
160 > 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")
161 >
162 > #
163 > # ChIP regions over the genome
164 > #
165 >
166 > par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
167 > layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5))
168 > x<-c(0.000000,0.000000)
169 > y<-c(0.000000,1.000000)
170 > 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)
171 > x<-c(0.000000,0.000000,0.000000,0.000000)
172 > y<-c(0.000000,0.000000,1.000000,1.000000)
173 > polygon(x,y,col=c("black"))
174 > x <- c(0.000000)
175 > y<-c(0.800000)
176 > lines(x, y,xlim=c(0, 0.0),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2)
177 > x<-c(0.000000,4127518.000000)
178 > y<-c(0.855556,1.144444)
179 > 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")
180 > start <- c(4119129)
181 > end <- c(4119130)
182 > vals <- c(0.0)
183 > vals[vals > 0.0] <- 0.0
184 > vals[vals < 0] <- 0
185 > heights <- 0.288889 * ((vals - 0)/(0.0 - 0)) + 0.855555555556
186 > for (i in 1:length(heights)) {
187 + 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"))
188 + }
189 > mtext("26",side=2,line=0,outer=FALSE,at=1.0)
190 > par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
191 > layout(matrix(c(1, 2, 3, 3, 4, 5), 3, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 1, 1))
192 > 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)
193 > 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)
194 > 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)
195 > abline(v=0.000000,lty=2,col=c("black"))
196 > 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)
197 > 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)
198 > 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)
199 > abline(v=0.000000,lty=2,col=c("black"))
200 > 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)
201 > 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)
202 > 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)
203 > abline(v=0.000000,lty=2,col=c("black"))
204 > abline(v=3000.000000,lty=2,col=c("black"))
205 > 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)
206 > 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)
207 > 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)
208 > 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)
209 > 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)
210 > 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)
211 > par(mfrow=c(3, 2),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
212 > x<-c(-3.000000,3.000000)
213 > y<-c(0.000000,1.000000)
214 > 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")
215 > x<-c(-3.000000,3.000000)
216 > y<-c(0.000000,1.000000)
217 > 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")
218 > x<-c(-3.000000,3.000000)
219 > y<-c(0.000000,1.000000)
220 > 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")
221 > x<-c(-3.000000,3.000000)
222 > y<-c(0.000000,1.000000)
223 > 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")
224 > x<-c(-3.000000,3.000000)
225 > y<-c(0.000000,1.000000)
226 > 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")
227 > 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)
228 > 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)
229 > plot(x, y,type="l",main="Average Intron Profile
230 + (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)
231 > dev.off()
232 null device
233 1
234 >
235 INFO @ Tue, 23 Jun 2015 13:03:33: #... cong! See ceas.pdf for the graphical results of CEAS!