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1 ceas -- 0.9.9.7 (package version 1.0.2)
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2 INFO @ Tue, 23 Jun 2015 09:12:22:
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3 # ARGUMENTS:
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4 # name = ceas
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5 # gene annotation table = galGal3.refGene
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6 # BED file = ceas_in.bed
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7 # WIG file = None
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8 # extra BED file = None
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9 # ChIP annotation = On
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10 # gene-centered annotation = On
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11 # average profiling = Off
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12 # dump profiles = Off
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13 # re-annotation for genome background (ChIP region annotation) = False
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14 # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
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15 # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
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16 # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
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17 # span size (gene-centered annotation) = 3000 bp
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18 INFO @ Tue, 23 Jun 2015 09:12:22: #1 read the gene table...
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19 INFO @ Tue, 23 Jun 2015 09:12:22: #2 read the bed file of ChIP regions...
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20 INFO @ Tue, 23 Jun 2015 09:12:22: #3 perform gene-centered annotation...
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21 INFO @ Tue, 23 Jun 2015 09:12:22: #4 See ceas.xls for gene-centered annotation!
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22 INFO @ Tue, 23 Jun 2015 09:12:22: #5 read the pre-computed genome bg annotation...
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23 INFO @ Tue, 23 Jun 2015 09:12:22: #6 perform ChIP region annotation...
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24 INFO @ Tue, 23 Jun 2015 09:12:22: #7 write a R script of ChIP region annotation...
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25
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26 R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
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27 Copyright (C) 2014 The R Foundation for Statistical Computing
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28 Platform: x86_64-redhat-linux-gnu (64-bit)
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29
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30 R is free software and comes with ABSOLUTELY NO WARRANTY.
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31 You are welcome to redistribute it under certain conditions.
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32 Type 'license()' or 'licence()' for distribution details.
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33
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34 Natural language support but running in an English locale
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35
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36 R is a collaborative project with many contributors.
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37 Type 'contributors()' for more information and
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38 'citation()' on how to cite R or R packages in publications.
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39
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40 Type 'demo()' for some demos, 'help()' for on-line help, or
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41 'help.start()' for an HTML browser interface to help.
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42 Type 'q()' to quit R.
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43
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44 > # ARGUMENTS:
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45 > # name = ceas
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46 > # gene annotation table = galGal3.refGene
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47 > # BED file = ceas_in.bed
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48 > # WIG file = None
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49 > # extra BED file = None
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50 > # ChIP annotation = On
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51 > # gene-centered annotation = On
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52 > # average profiling = Off
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53 > # dump profiles = Off
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54 > # re-annotation for genome background (ChIP region annotation) = False
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55 > # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
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56 > # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
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57 > # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
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58 > # span size (gene-centered annotation) = 3000 bp
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59 > pdf("ceas.pdf",height=11.5,width=8.5)
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60 >
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61 > # 09:12:22 Tue, 23 Jun 2015
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62 > #
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63 > # ChIP annotation
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64 > #
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65 >
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66 >
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67 > #
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68 > # Chromosomal Distribution
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69 > #
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70 >
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71 > par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
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72 > r0<-c(100.0)
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73 > r1<-c(100.0)
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74 > height<-rbind(r0,r1)
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75 > names=c("26")
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76 > 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)
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77 > text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9)
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78 > text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9)
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79 > legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
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80 >
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81 > #
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82 > # Promoter,Bipromoter,Downstream, Gene and Regions of interest
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83 > #
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84 >
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85 > par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
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86 > r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416)
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87 > r1<-c(0.0, 0.0, 0.0)
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88 > height<-rbind(r0,r1)
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89 > names=c("<=1000 bp","<=2000 bp","<=3000 bp")
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90 > 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)
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91 > 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)
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92 > text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
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93 + (0.981)","0.000 %
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94 + (0.964)","0.000 %
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95 + (0.947)"),pos=3,offset=0.2)
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96 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
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97 > r0<-c(0.03876062889120376, 0.03876062889120376)
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98 > r1<-c(0.0, 0.0)
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99 > height<-rbind(r0,r1)
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100 > names=c("<=2500 bp","<=5000 bp")
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101 > 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)
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102 > text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2)
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103 > text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 %
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104 + (1.000)","0.000 %
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105 + (1.000)"),pos=3,offset=0.2)
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106 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
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107 > r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683)
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108 > r1<-c(0.0, 0.0, 0.0)
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109 > height<-rbind(r0,r1)
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110 > names=c("<=1000 bp","<=2000 bp","<=3000 bp")
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111 > 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)
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112 > 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)
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113 > text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
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114 + (0.982)","0.000 %
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115 + (0.965)","0.000 %
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116 + (0.950)"),pos=3,offset=0.2)
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117 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
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118 > r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054)
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119 > r1<-c(0.0, 0.0, 0.0, 0.0, 0.0)
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120 > height<-rbind(r0,r1)
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121 > names=c("5'UTR","3'UTR","Coding Exon","Intron","All")
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122 > 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)
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123 > 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)
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124 > text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 %
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125 + (0.998)","0.000 %
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126 + (0.986)","0.000 %
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127 + (0.976)","0.000 %
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128 + (0.803)","0.000 %
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129 + (0.763)"),pos=3,offset=0.2)
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130 > legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
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131 >
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132 > #
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133 > # Distribution of Genome and ChIP regions over cis-regulatory element
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134 > # Note that the x may be modified for better graphics in case a value is too small
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135 > # Thus, look at the labels of the pie chart to get the real percentage values
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136 > #
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137 >
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138 > par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2))
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139 > x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292)
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140 > 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)
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141 > x<-c(0.000000,1.000000)
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142 > y<-c(0.000000,1.000000)
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143 > plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
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144 > 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")
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145 > x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000)
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146 > 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)
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147 > x<-c(0.000000,1.000000)
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148 > y<-c(0.000000,1.000000)
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149 > plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
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150 > 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")
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151 >
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152 > #
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153 > # ChIP regions over the genome
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154 > #
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155 >
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156 > par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
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157 > layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5))
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158 > x<-c(0.000000,2.515610)
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159 > y<-c(0.000000,1.000000)
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160 > plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,2.515610),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9)
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161 > x<-c(0.000000,2.515610,2.515610,0.000000)
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162 > y<-c(0.000000,0.000000,1.000000,1.000000)
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163 > polygon(x,y,col=c("black"))
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164 > x <- c(0.000000,0.169726,0.339451,0.509177,0.678903,0.848628,1.018354,1.188079,1.357805,1.527531,1.697256,1.866982,2.036708,2.206433,2.376159)
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165 > y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.800000)
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166 > lines(x, y,xlim=c(0, 2.51561),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2)
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167 > x<-c(4119129.000000,4119130.000000)
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168 > y<-c(0.855556,1.144444)
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169 > plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(4119129.000000,4119130.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n")
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170 > start <- c(4119129)
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171 > end <- c(4119130)
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172 > vals <- c(2.51561)
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173 > vals[vals > 2.51561] <- 2.51561
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174 > vals[vals < 0] <- 0
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175 > heights <- 0.288889 * ((vals - 0)/(2.51561 - 0)) + 0.855555555556
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176 > for (i in 1:length(heights)) {
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177 + 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"))
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178 + }
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179 > mtext("26",side=2,line=0,outer=FALSE,at=1.0)
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180 > dev.off()
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181 null device
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182 1
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183 >
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184 INFO @ Tue, 23 Jun 2015 09:12:22: #... cong! See ceas.pdf for the graphical results of CEAS!
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