├── .RData ├── .Rhistory ├── .dropbox ├── DATA ├── APPLE │ ├── Apple1.csv │ ├── Apple2.csv │ └── Apple3.csv ├── EX_HELP_PAGE │ ├── Example_Data_Set1.csv │ ├── Example_Data_Set2.csv │ ├── Example_Data_Set3.txt │ ├── titi.txt │ └── toto.txt ├── SMALL_DATASET │ ├── titi │ ├── tktk │ ├── toto │ ├── tutu │ └── tyty ├── SORGHUM │ ├── CIRAD │ ├── S2 │ ├── S4 │ ├── S5 │ ├── S6 │ └── TAMU ├── WHEAT_MACAF │ └── CLEAN │ │ ├── Ben_Pi41025 │ │ ├── Colosseo_Lloyd │ │ ├── Kofa_svevo │ │ ├── Langdon_G1816 │ │ ├── Latino_MG5323 │ │ ├── Mohawk_Cocorrit69 │ │ ├── Simeto_Levante │ │ ├── Simeto_MoliseColi │ │ ├── Svevo_Ciccio │ │ ├── W9292-260D3_Kofa │ │ └── Zvevo_Zavitan └── WHEAT_TRAM │ ├── map_DL │ ├── map_DS │ ├── map_consensus │ └── physical_position ├── LEGEND ├── legend_sheet1.txt ├── legend_sheet2.txt ├── legend_sheet3.txt ├── legend_sheet4.txt ├── legend_sheet5.txt └── legend_sheet6.txt ├── README.md ├── RESSOURCES ├── Figure1.jpg ├── INRA_logo - copie.jpg ├── INRA_logo.jpg ├── LOGO_genmapcomp.png ├── Logo_GenMap_Small.png ├── Logo_supagro.jpg ├── donut_function.R └── tmp ├── global.R ├── server.R ├── ui.R └── www ├── Rscript_for_background_image_sheet1 ├── genComp.css ├── logo-institut-agro-montpellier.png ├── logo_INRAE.png ├── logo_INRA_old.png ├── logo_SUPAGRO.jpg ├── logo_arvalis.png ├── map_montpellier.png └── my_image.png /.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/.RData -------------------------------------------------------------------------------- /.Rhistory: -------------------------------------------------------------------------------- 1 | DL_MATRIX_K2 <- read.table("~/MesDocuments/cloud/Dropbox/2013-2014/recherche_2013_2014/EPO/DL_MATRIX_K2.txt", header=T, quote="\"") 2 | View(DL_MATRIX_K2) 3 | install.packages("igraph") 4 | graph.adjacency 5 | ?graph 6 | ?igraph 7 | library(markdown) 8 | updater() 9 | install.packages("installr") 10 | install.packages("installr") 11 | title: "test" 12 | cwd 13 | setwd(/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir) 14 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir") 15 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir/") 16 | setwd(/Users/ranwez/MesDocuments/cloud/Dropbox/) 17 | setwd('/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir/') 18 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir/") 19 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparatordir") 20 | setwd("/Users/ranwez/MesDocuments/cloud/") 21 | ls() 22 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/") 23 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap\-Comparatordir") 24 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap\-Comparatordir") 25 | dir() 26 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/GenMap-Comparator") 27 | ls 28 | ls() 29 | dir() 30 | library(shiny) 31 | runApp("GenMap-Comparator") 32 | setwd("/Users/ranwez/MesDocuments/cloud/Dropbox/") 33 | runApp("GenMap-Comparator") 34 | runApp("GenMap-Comparator") 35 | runApp("GenMap-Comparator") 36 | runApp("GenMap-Comparator") 37 | runApp("GenMap-Comparator") 38 | runApp("GenMap-Comparator") 39 | runApp("GenMap-Comparator") 40 | runApp("GenMap-Comparator") 41 | runApp("GenMap-Comparator") 42 | runApp("GenMap-Comparator") 43 | is.null(()) 44 | is.null(c()) 45 | runApp("GenMap-Comparator") 46 | runApp("GenMap-Comparator") 47 | runApp("GenMap-Comparator") 48 | runApp("GenMap-Comparator") 49 | runApp("GenMap-Comparator") 50 | runApp("GenMap-Comparator") 51 | runApp("GenMap-Comparator") 52 | runApp("GenMap-Comparator") 53 | runApp("GenMap-Comparator") 54 | runApp("GenMap-Comparator") 55 | runApp("GenMap-Comparator") 56 | runApp("GenMap-Comparator") 57 | runApp("GenMap-Comparator") 58 | runApp("GenMap-Comparator") 59 | runApp("GenMap-Comparator") 60 | runApp("GenMap-Comparator") 61 | runApp("GenMap-Comparator") 62 | runApp("GenMap-Comparator") 63 | runApp("GenMap-Comparator") 64 | runApp("GenMap-Comparator") 65 | runApp("GenMap-Comparator") 66 | runApp("GenMap-Comparator") 67 | runApp("GenMap-Comparator") 68 | runApp("GenMap-Comparator") 69 | runApp("GenMap-Comparator") 70 | runApp("GenMap-Comparator") 71 | runApp("GenMap-Comparator") 72 | runApp("GenMap-Comparator") 73 | runApp("GenMap-Comparator") 74 | runApp("GenMap-Comparator") 75 | runApp("GenMap-Comparator") 76 | runApp("GenMap-Comparator") 77 | runApp("GenMap-Comparator") 78 | runApp("GenMap-Comparator") 79 | runApp("GenMap-Comparator") 80 | runApp("GenMap-Comparator") 81 | runApp("GenMap-Comparator") 82 | runApp("GenMap-Comparator") 83 | runApp("GenMap-Comparator") 84 | runApp("GenMap-Comparator") 85 | runApp("GenMap-Comparator") 86 | runApp("GenMap-Comparator") 87 | runApp("GenMap-Comparator") 88 | runApp("GenMap-Comparator") 89 | runApp("GenMap-Comparator") 90 | runApp("GenMap-Comparator") 91 | runApp("GenMap-Comparator") 92 | runApp("GenMap-Comparator") 93 | runApp("GenMap-Comparator") 94 | runApp("GenMap-Comparator") 95 | runApp("GenMap-Comparator") 96 | runApp("GenMap-Comparator") 97 | runApp("GenMap-Comparator") 98 | runApp("GenMap-Comparator") 99 | runApp("GenMap-Comparator") 100 | runApp("GenMap-Comparator") 101 | runApp("GenMap-Comparator") 102 | runApp("GenMap-Comparator") 103 | runApp("GenMap-Comparator") 104 | runApp("GenMap-Comparator") 105 | runApp("GenMap-Comparator") 106 | runApp("GenMap-Comparator") 107 | runApp("GenMap-Comparator") 108 | runApp("GenMap-Comparator") 109 | runApp("GenMap-Comparator") 110 | runApp("GenMap-Comparator") 111 | runApp("GenMap-Comparator") 112 | runApp("GenMap-Comparator") 113 | runApp("GenMap-Comparator") 114 | legend1=read.table("LEGEND/legend_sheet1.txt",sep="@")[,2] 115 | legend1=read.table("LEGEND/legend_sheet1.txt",sep="@")[,2] 116 | runApp("GenMap-Comparator") 117 | runApp("GenMap-Comparator") 118 | runApp("GenMap-Comparator") 119 | runApp("GenMap-Comparator") 120 | runApp("GenMap-Comparator") 121 | runApp("GenMap-Comparator") 122 | runApp("GenMap-Comparator") 123 | legend2 124 | runApp("GenMap-Comparator") 125 | runApp("GenMap-Comparator") 126 | runApp("GenMap-Comparator") 127 | runApp("GenMap-Comparator") 128 | runApp("GenMap-Comparator") 129 | runApp("GenMap-Comparator") 130 | runApp("GenMap-Comparator") 131 | runApp("GenMap-Comparator") 132 | runApp("GenMap-Comparator") 133 | runApp("GenMap-Comparator") 134 | runApp("GenMap-Comparator") 135 | runApp("GenMap-Comparator") 136 | runApp("GenMap-Comparator") 137 | runApp("GenMap-Comparator") 138 | runApp("GenMap-Comparator") 139 | runApp("GenMap-Comparator") 140 | runApp("GenMap-Comparator") 141 | runApp("GenMap-Comparator") 142 | runApp("GenMap-Comparator") 143 | runApp("GenMap-Comparator") 144 | runApp("GenMap-Comparator") 145 | runApp("GenMap-Comparator") 146 | runApp("GenMap-Comparator") 147 | runApp("GenMap-Comparator") 148 | runApp("GenMap-Comparator") 149 | runApp("GenMap-Comparator") 150 | runApp("GenMap-Comparator") 151 | runApp("GenMap-Comparator") 152 | runApp("GenMap-Comparator") 153 | runApp("GenMap-Comparator") 154 | runApp("GenMap-Comparator") 155 | runApp("GenMap-Comparator") 156 | runApp("GenMap-Comparator") 157 | runApp("GenMap-Comparator") 158 | runApp("GenMap-Comparator") 159 | runApp("GenMap-Comparator") 160 | runApp("GenMap-Comparator") 161 | runApp("GenMap-Comparator") 162 | runApp("GenMap-Comparator") 163 | runApp("GenMap-Comparator") 164 | runApp("GenMap-Comparator") 165 | runApp("GenMap-Comparator") 166 | runApp("GenMap-Comparator") 167 | runApp("GenMap-Comparator") 168 | runApp("GenMap-Comparator") 169 | runApp("GenMap-Comparator") 170 | runApp("GenMap-Comparator") 171 | runApp("GenMap-Comparator") 172 | runApp("GenMap-Comparator") 173 | runApp("GenMap-Comparator") 174 | runApp("GenMap-Comparator") 175 | runApp("GenMap-Comparator") 176 | runApp("GenMap-Comparator") 177 | runApp("GenMap-Comparator") 178 | runApp("GenMap-Comparator") 179 | runApp("GenMap-Comparator") 180 | runApp("GenMap-Comparator") 181 | runApp("GenMap-Comparator") 182 | runApp("GenMap-Comparator") 183 | runApp("GenMap-Comparator") 184 | runApp("GenMap-Comparator") 185 | runApp("GenMap-Comparator") 186 | runApp("GenMap-Comparator") 187 | runApp("GenMap-Comparator") 188 | runApp("GenMap-Comparator") 189 | runApp("GenMap-Comparator") 190 | runApp("GenMap-Comparator") 191 | runApp("GenMap-Comparator") 192 | runApp("GenMap-Comparator") 193 | runApp("GenMap-Comparator") 194 | runApp("GenMap-Comparator") 195 | runApp("GenMap-Comparator") 196 | runApp("GenMap-Comparator") 197 | runApp("GenMap-Comparator") 198 | runApp("GenMap-Comparator") 199 | runApp("GenMap-Comparator") 200 | runApp("GenMap-Comparator") 201 | runApp("GenMap-Comparator") 202 | runApp("GenMap-Comparator") 203 | runApp("GenMap-Comparator") 204 | runApp("GenMap-Comparator") 205 | runApp("GenMap-Comparator") 206 | runApp("GenMap-Comparator") 207 | runApp("GenMap-Comparator") 208 | runApp("GenMap-Comparator") 209 | runApp("GenMap-Comparator") 210 | runApp("GenMap-Comparator") 211 | runApp("GenMap-Comparator") 212 | runApp("GenMap-Comparator") 213 | runApp("GenMap-Comparator") 214 | runApp("GenMap-Comparator") 215 | runApp("GenMap-Comparator") 216 | runApp("GenMap-Comparator") 217 | runApp("GenMap-Comparator") 218 | runApp("GenMap-Comparator") 219 | runApp("GenMap-Comparator") 220 | runApp("GenMap-Comparator") 221 | runApp("GenMap-Comparator") 222 | runApp("GenMap-Comparator") 223 | runApp("GenMap-Comparator") 224 | runApp("GenMap-Comparator") 225 | runApp("GenMap-Comparator") 226 | runApp("GenMap-Comparator") 227 | runApp("GenMap-Comparator") 228 | runApp("GenMap-Comparator") 229 | runApp("GenMap-Comparator") 230 | runApp("GenMap-Comparator") 231 | runApp("GenMap-Comparator") 232 | runApp("GenMap-Comparator") 233 | runApp("GenMap-Comparator") 234 | runApp("GenMap-Comparator") 235 | runApp("GenMap-Comparator") 236 | runApp("GenMap-Comparator") 237 | runApp("GenMap-Comparator") 238 | runApp("GenMap-Comparator") 239 | runApp("GenMap-Comparator") 240 | ? 241 | quit() 242 | ?<- 243 | ?variable.names 244 | runApp("GenMap-Comparator") 245 | runApp("GenMap-Comparator") 246 | runApp("GenMap-Comparator") 247 | -------------------------------------------------------------------------------- /.dropbox: -------------------------------------------------------------------------------- 1 | {"tag": "shared", "ns": 1165075255} -------------------------------------------------------------------------------- /DATA/APPLE/Apple1.csv: -------------------------------------------------------------------------------- 1 | group;LG1 2 | Map_1_1;0.000 3 | Map_1_2;1.275 4 | Map_1_3;1.412 5 | Mrk_17036;3.743 6 | Map_1_4;4.765 7 | Mrk_16976;5.038 8 | Map_1_5;9.671 9 | Mrk_16756;12.513 10 | Mrk_15204;12.683 11 | Map_1_6;14.325 12 | Map_1_7;14.425 13 | Map_1_8;14.427 14 | Map_1_9;14.429 15 | Mrk_16667;14.434 16 | Map_1_10;14.465 17 | Mrk_16703;14.471 18 | Map_1_11;16.199 19 | Map_1_12;17.359 20 | Map_1_13;17.365 21 | Map_1_14;17.365 22 | Map_1_15;17.367 23 | Map_1_16;18.537 24 | Map_1_17;18.550 25 | Mrk_16437;18.550 26 | Mrk_16433;18.555 27 | Map_1_18;20.460 28 | Mrk_16417;20.892 29 | Mrk_17062;21.493 30 | Mrk_16185;22.700 31 | Mrk_16081;24.506 32 | Mrk_16051;28.803 33 | Mrk_15912;29.414 34 | Mrk_15895;29.423 35 | Map_1_19;30.608 36 | Mrk_15839;30.627 37 | Map_1_20;32.544 38 | Mrk_15801;33.725 39 | Mrk_15604;38.672 40 | Mrk_15612;38.695 41 | Map_1_21;39.705 42 | Mrk_15572;39.763 43 | Mrk_15560;40.903 44 | Map_1_22;42.687 45 | Mrk_15516;43.207 46 | Map_1_23;43.228 47 | Map_1_24;47.928 48 | Mrk_15152;49.309 49 | Map_1_25;49.369 50 | Map_1_26;50.902 51 | Map_1_27;51.047 52 | group;LG2 53 | Map_1_29;0.000 54 | Mrk_17252;0.561 55 | Mrk_17269;1.043 56 | Mrk_17183;4.349 57 | Mrk_17102;4.898 58 | Mrk_17105;4.898 59 | Map_1_30;8.888 60 | Map_1_31;9.463 61 | Mrk_17106;10.750 62 | Map_1_32;14.004 63 | Map_1_33;14.736 64 | Mrk_17272;14.778 65 | Mrk_17249;14.805 66 | Map_1_34;15.919 67 | Mrk_17185;15.942 68 | Map_1_35;17.657 69 | Map_1_36;18.595 70 | Mrk_16812;18.928 71 | Map_1_37;18.939 72 | Map_1_38;19.263 73 | Mrk_16674;20.738 74 | Mrk_16468;21.653 75 | Map_1_39;21.667 76 | Mrk_16469;21.667 77 | Mrk_16473;21.716 78 | Mrk_16421;23.465 79 | Map_1_40;23.528 80 | Map_1_41;23.916 81 | Mrk_16389;23.983 82 | Mrk_16323;25.459 83 | Mrk_14850;25.555 84 | Mrk_16322;25.632 85 | Map_1_42;25.705 86 | Mrk_16273;27.413 87 | Map_1_43;27.960 88 | Map_1_44;28.087 89 | Map_1_45;28.381 90 | Map_1_46;29.961 91 | Map_1_47;30.696 92 | Mrk_14873;31.710 93 | Mrk_15842;33.438 94 | Mrk_15844;33.438 95 | Mrk_15793;33.841 96 | Map_1_48;33.906 97 | Mrk_14929;33.906 98 | Mrk_15660;34.801 99 | Map_1_49;35.612 100 | Mrk_15668;35.941 101 | Mrk_15719;35.962 102 | Mrk_14723;36.474 103 | Mrk_15664;36.474 104 | Mrk_15662;36.794 105 | Mrk_15205;37.280 106 | Map_1_50;37.689 107 | Map_1_51;37.846 108 | Mrk_15598;38.657 109 | Mrk_15599;38.660 110 | Mrk_15597;38.660 111 | Mrk_15618;38.663 112 | Mrk_15614;38.670 113 | Map_1_52;38.670 114 | Map_1_53;39.566 115 | Mrk_14665;39.616 116 | Mrk_15558;40.334 117 | Mrk_15545;40.404 118 | Mrk_14721;40.975 119 | Mrk_15551;41.579 120 | Map_1_54;41.771 121 | Mrk_15544;42.141 122 | Mrk_14970;42.721 123 | Mrk_15510;43.921 124 | Mrk_15389;44.479 125 | Mrk_15354;44.929 126 | Mrk_15338;45.054 127 | Mrk_15331;45.089 128 | Mrk_15379;45.089 129 | Mrk_14808;45.323 130 | Mrk_15335;46.007 131 | Mrk_15355;46.191 132 | Mrk_15314;46.276 133 | Mrk_15318;46.280 134 | Map_1_55;46.285 135 | Mrk_14885;46.430 136 | Map_1_56;46.456 137 | Mrk_15353;46.789 138 | Map_1_57;47.824 139 | Mrk_14883;48.406 140 | Mrk_17924;49.163 141 | Map_1_58;49.417 142 | Mrk_14914;49.530 143 | Map_1_59;49.533 144 | Mrk_15266;49.562 145 | Mrk_15267;49.566 146 | Mrk_17893;50.054 147 | Map_1_60;50.057 148 | Mrk_17889;50.059 149 | Map_1_61;50.068 150 | Mrk_17894;50.312 151 | Mrk_17890;50.935 152 | Map_1_62;51.173 153 | Map_1_63;51.482 154 | Map_1_64;51.568 155 | Mrk_17739;51.702 156 | Mrk_17734;52.272 157 | Mrk_17733;52.648 158 | Mrk_17692;53.229 159 | Map_1_65;53.243 160 | Map_1_66;53.410 161 | Mrk_17657;53.890 162 | Mrk_17639;54.315 163 | Mrk_17648;54.395 164 | Map_1_67;54.447 165 | Mrk_17630;54.775 166 | Mrk_17631;54.801 167 | Mrk_14901;54.945 168 | Map_1_68;54.964 169 | Map_1_69;55.124 170 | Mrk_17650;55.132 171 | Mrk_14744;55.608 172 | Mrk_17632;55.670 173 | Mrk_17608;55.718 174 | Map_1_70;56.202 175 | Map_1_71;56.203 176 | Mrk_17548;56.203 177 | Mrk_17611;56.732 178 | Map_1_72;56.844 179 | Map_1_73;57.227 180 | Map_1_74;58.569 181 | Mrk_17480;58.569 182 | Mrk_17482;58.571 183 | Mrk_17446;59.097 184 | Map_1_75;59.206 185 | Mrk_17448;59.206 186 | Map_1_76;60.028 187 | Map_1_77;61.252 188 | Mrk_17404;61.261 189 | Map_1_78;62.689 190 | Mrk_17361;62.754 191 | Map_1_79;62.824 192 | Map_1_80;62.929 193 | Mrk_17347;63.352 194 | Map_1_81;63.444 195 | Map_1_82;63.994 196 | Map_1_83;63.994 197 | Map_1_84;64.006 198 | Mrk_17341;64.276 199 | Mrk_17322;65.152 200 | Map_1_85;65.601 201 | Mrk_17307;65.644 202 | Map_1_86;66.117 203 | Mrk_14949;66.117 204 | Map_1_87;66.263 205 | Mrk_17283;66.263 206 | Map_1_88;66.680 207 | Mrk_14997;67.244 208 | Mrk_16997;67.278 209 | Mrk_16875;67.293 210 | Mrk_16827;67.891 211 | Mrk_16649;68.525 212 | Map_1_89;69.745 213 | Map_1_90;70.292 214 | Map_1_91;70.592 215 | Mrk_15984;70.755 216 | Map_1_92;71.392 217 | Mrk_15908;71.561 218 | Mrk_15929;71.719 219 | Mrk_4;72.127 220 | Map_1_93;72.825 221 | Mrk_15485;73.356 222 | Mrk_14896;73.380 223 | Mrk_15445;75.145 224 | group;LG3 225 | Map_1_95;0.000 226 | Map_1_96;1.089 227 | Map_1_97;2.039 228 | Mrk_14747;2.339 229 | Mrk_17207;2.339 230 | Map_1_98;2.864 231 | Mrk_17189;4.100 232 | Mrk_17190;4.100 233 | Mrk_17198;4.234 234 | Mrk_17167;5.280 235 | Mrk_17192;5.349 236 | Mrk_17193;5.632 237 | Mrk_15049;7.045 238 | Mrk_17154;8.026 239 | Map_1_99;8.165 240 | Mrk_17116;9.896 241 | Mrk_17113;9.993 242 | Mrk_17115;10.007 243 | Mrk_17129;10.007 244 | Mrk_17111;10.588 245 | Map_1_100;11.184 246 | Map_1_101;12.624 247 | Map_1_102;13.013 248 | Mrk_14934;13.351 249 | Map_1_103;13.632 250 | Map_1_104;14.171 251 | Mrk_16908;14.213 252 | Map_1_105;14.763 253 | Mrk_16760;16.916 254 | Map_1_106;17.468 255 | Map_1_107;17.555 256 | Mrk_15032;17.704 257 | Mrk_16640;20.051 258 | Mrk_16642;20.051 259 | Mrk_16602;21.242 260 | Mrk_16466;24.635 261 | Mrk_16467;24.780 262 | Map_1_108;25.837 263 | Map_1_109;25.840 264 | Mrk_16271;26.745 265 | Map_1_110;26.845 266 | Map_1_111;27.195 267 | Mrk_16206;27.203 268 | Map_1_112;27.208 269 | Map_1_113;27.210 270 | Map_1_114;27.222 271 | Mrk_15388;28.175 272 | Mrk_15339;28.437 273 | Mrk_15165;28.609 274 | Mrk_15760;28.621 275 | Map_1_115;29.373 276 | Map_1_116;29.373 277 | Mrk_14715;29.740 278 | Mrk_15594;30.239 279 | Map_1_117;30.567 280 | Mrk_15100;30.768 281 | Map_1_118;31.749 282 | Mrk_15265;34.200 283 | Map_1_119;37.120 284 | Map_1_120;37.730 285 | Mrk_17908;38.640 286 | Mrk_17909;39.514 287 | Map_1_121;41.933 288 | Map_1_122;43.335 289 | Map_1_123;43.396 290 | Map_1_124;43.597 291 | Map_1_125;44.739 292 | Mrk_17647;44.750 293 | Mrk_17642;45.848 294 | Mrk_17599;48.539 295 | Mrk_17591;48.551 296 | Map_1_126;49.436 297 | Mrk_15946;55.803 298 | Map_1_127;56.090 299 | Mrk_15987;56.119 300 | Mrk_15990;56.161 301 | Mrk_15713;56.197 302 | Map_1_128;56.488 303 | Mrk_15823;56.488 304 | Map_1_129;57.307 305 | Mrk_17694;57.319 306 | Mrk_17687;57.343 307 | Map_1_130;57.362 308 | Mrk_15258;57.601 309 | Mrk_15304;57.616 310 | Mrk_15317;57.924 311 | Map_1_131;58.065 312 | Mrk_14792;58.325 313 | Mrk_17754;59.673 314 | Map_1_132;62.598 315 | Mrk_16428;62.728 316 | Map_1_133;62.753 317 | group;LG4 318 | Map_1_135;0.000 319 | Map_1_136;0.243 320 | Map_1_137;0.459 321 | Map_1_138;1.084 322 | Map_1_139;1.660 323 | Map_1_140;2.268 324 | Map_1_141;2.557 325 | Mrk_14985;6.183 326 | Map_1_142;12.352 327 | Mrk_17244;14.029 328 | Mrk_17376;17.879 329 | Mrk_17400;17.886 330 | Mrk_17375;17.897 331 | Map_1_143;18.423 332 | Map_1_144;21.426 333 | Mrk_17726;22.948 334 | Map_1_145;23.216 335 | Map_1_146;24.367 336 | Mrk_17730;25.408 337 | Map_1_147;26.839 338 | Map_1_148;27.326 339 | Mrk_15430;27.350 340 | Mrk_15281;27.418 341 | Mrk_15283;27.428 342 | Mrk_15282;27.440 343 | Mrk_15068;27.707 344 | Mrk_15429;28.684 345 | Map_1_149;29.257 346 | Map_1_150;29.571 347 | Mrk_15533;30.671 348 | Map_1_151;30.682 349 | Mrk_15541;31.273 350 | Mrk_15567;32.216 351 | Map_1_152;32.348 352 | Mrk_15030;32.349 353 | Mrk_14923;32.356 354 | Map_1_153;32.383 355 | Mrk_15565;32.399 356 | Map_1_154;32.823 357 | Mrk_14892;34.187 358 | Map_1_155;35.036 359 | Map_1_156;35.227 360 | Map_1_157;35.850 361 | Mrk_15613;36.655 362 | Map_1_158;39.538 363 | Mrk_15721;39.949 364 | Map_1_159;41.764 365 | Mrk_14864;45.428 366 | Mrk_15978;46.504 367 | Map_1_160;47.445 368 | Mrk_16068;52.671 369 | Mrk_16054;57.094 370 | Map_1_161;57.757 371 | Mrk_16123;57.759 372 | Map_1_162;58.282 373 | Map_1_163;58.316 374 | Mrk_16220;60.146 375 | Mrk_14828;60.147 376 | Mrk_16256;61.351 377 | Mrk_16272;61.805 378 | Mrk_14740;61.955 379 | Mrk_16296;62.210 380 | Mrk_16291;62.625 381 | Map_1_164;62.625 382 | Mrk_16279;62.861 383 | Mrk_14724;63.308 384 | Map_1_165;63.442 385 | Map_1_166;63.488 386 | Mrk_16253;63.508 387 | Map_1_167;64.268 388 | Mrk_16242;64.712 389 | Mrk_16238;65.039 390 | group;LG5 391 | Map_1_169;0.000 392 | Map_1_170;7.661 393 | Map_1_171;7.661 394 | Mrk_16989;8.992 395 | Mrk_16990;9.040 396 | Mrk_16899;11.584 397 | Map_1_172;13.388 398 | Mrk_15183;14.948 399 | Mrk_16753;15.957 400 | Mrk_14967;16.196 401 | Mrk_15021;16.462 402 | Mrk_15060;17.984 403 | Map_1_173;19.856 404 | Map_1_174;22.446 405 | Map_1_175;22.450 406 | Map_1_176;23.058 407 | Map_1_177;23.912 408 | Map_1_178;24.289 409 | Map_1_179;24.291 410 | Mrk_15771;24.543 411 | Mrk_16090;24.713 412 | Map_1_180;25.086 413 | Mrk_16089;27.356 414 | Mrk_16014;27.405 415 | Mrk_16096;27.412 416 | Mrk_16012;28.128 417 | Map_1_181;30.223 418 | Mrk_15891;34.475 419 | Mrk_15555;39.154 420 | Map_1_182;42.717 421 | Mrk_15071;49.187 422 | Mrk_14912;51.218 423 | Map_1_183;51.228 424 | Mrk_17697;52.052 425 | Map_1_184;52.108 426 | Mrk_15586;52.109 427 | Map_1_185;52.209 428 | Mrk_15588;52.219 429 | Mrk_15606;52.258 430 | Mrk_15605;52.306 431 | Mrk_15081;52.431 432 | Map_1_186;52.729 433 | Mrk_17860;53.271 434 | Map_1_187;56.244 435 | Map_1_188;56.492 436 | Map_1_189;56.492 437 | Map_1_190;56.527 438 | Mrk_17602;58.753 439 | Map_1_191;60.938 440 | Map_1_192;61.296 441 | Map_1_193;61.296 442 | Map_1_194;61.298 443 | Map_1_195;61.425 444 | Map_1_196;61.676 445 | Mrk_17326;64.676 446 | Mrk_17324;64.820 447 | Mrk_17334;64.839 448 | Mrk_17311;65.294 449 | Map_1_197;65.400 450 | Mrk_17315;65.498 451 | Mrk_15156;66.707 452 | Map_1_198;69.543 453 | Mrk_16299;69.543 454 | Mrk_15421;69.548 455 | Mrk_16261;70.087 456 | Mrk_17961;70.123 457 | Map_1_199;72.055 458 | Mrk_15483;72.055 459 | Mrk_17580;72.358 460 | Mrk_17955;72.788 461 | Map_1_200;73.727 462 | group;LG6 463 | Map_1_202;0.000 464 | Map_1_203;1.398 465 | Mrk_16381;5.867 466 | Mrk_15191;6.313 467 | Mrk_16865;6.431 468 | Map_1_204;7.274 469 | Map_1_205;7.295 470 | Map_1_206;7.519 471 | Mrk_17239;7.646 472 | Mrk_17243;7.646 473 | Mrk_17286;7.965 474 | Mrk_14711;8.245 475 | Mrk_17304;8.252 476 | Mrk_17368;8.529 477 | Mrk_17362;8.529 478 | Map_1_207;9.053 479 | Mrk_17396;9.088 480 | Mrk_17395;9.094 481 | Map_1_208;9.715 482 | Mrk_17781;9.718 483 | Mrk_17469;9.719 484 | Mrk_17784;9.746 485 | Map_1_209;9.775 486 | Mrk_17474;10.275 487 | Map_1_210;10.310 488 | Mrk_17478;10.585 489 | Map_1_211;11.202 490 | Mrk_14682;11.446 491 | Mrk_15776;11.568 492 | Map_1_212;11.595 493 | Mrk_17601;11.974 494 | Mrk_17656;14.489 495 | Map_1_213;16.444 496 | Map_1_214;18.204 497 | Map_1_215;18.602 498 | Mrk_14843;18.794 499 | Map_1_216;18.794 500 | Mrk_15988;18.820 501 | Map_1_217;18.886 502 | Mrk_14786;23.298 503 | Map_1_218;26.321 504 | Mrk_16058;28.629 505 | Mrk_14686;31.552 506 | Map_1_219;32.240 507 | Mrk_16228;38.336 508 | Mrk_16148;39.658 509 | Mrk_14972;40.185 510 | Mrk_16234;41.559 511 | Map_1_220;42.711 512 | Map_1_221;42.806 513 | Mrk_15003;46.860 514 | Mrk_14948;49.672 515 | Mrk_16354;52.482 516 | Mrk_16364;52.508 517 | Mrk_14960;52.663 518 | Mrk_16369;53.787 519 | Mrk_16368;53.811 520 | Map_1_222;54.544 521 | Map_1_223;59.090 522 | Mrk_16477;59.904 523 | Mrk_16517;60.446 524 | Mrk_15004;61.185 525 | Mrk_16553;64.225 526 | Mrk_16559;64.232 527 | Map_1_224;64.242 528 | Mrk_16560;64.252 529 | Mrk_16573;64.825 530 | Map_1_225;64.833 531 | Map_1_226;65.053 532 | Mrk_16574;65.446 533 | Mrk_16577;65.519 534 | Mrk_16654;67.780 535 | Mrk_16659;67.929 536 | Mrk_16658;70.644 537 | group;LG7 538 | Map_1_228;0.000 539 | Mrk_17789;2.300 540 | Map_1_229;3.488 541 | Mrk_16545;4.608 542 | Mrk_16542;4.693 543 | Mrk_16568;4.693 544 | Mrk_15321;5.210 545 | Map_1_230;5.228 546 | Mrk_17048;7.008 547 | Map_1_231;7.331 548 | Map_1_232;7.567 549 | Mrk_17227;8.127 550 | Mrk_17336;9.356 551 | Map_1_233;9.356 552 | Map_1_234;11.096 553 | Map_1_235;11.244 554 | Mrk_17540;12.241 555 | Map_1_236;14.676 556 | Mrk_17057;15.169 557 | Mrk_17745;15.295 558 | Map_1_237;15.821 559 | Map_1_238;15.831 560 | Map_1_239;15.941 561 | Mrk_17813;16.467 562 | Mrk_17340;16.490 563 | Map_1_240;16.503 564 | Mrk_17832;18.144 565 | Mrk_16637;18.656 566 | Map_1_241;18.749 567 | Map_1_242;21.851 568 | Map_1_243;22.226 569 | Mrk_15066;22.467 570 | Map_1_244;22.564 571 | Map_1_245;22.564 572 | Mrk_15311;23.546 573 | Mrk_15141;23.561 574 | Map_1_246;23.671 575 | Map_1_247;24.005 576 | Mrk_15345;24.085 577 | Mrk_15194;24.641 578 | Map_1_248;24.725 579 | Map_1_249;25.197 580 | Map_1_250;25.408 581 | Map_1_251;26.954 582 | Mrk_14928;26.956 583 | Map_1_252;26.986 584 | Map_1_253;26.986 585 | Map_1_254;27.023 586 | Mrk_15873;27.240 587 | Mrk_15199;27.511 588 | Mrk_15176;27.524 589 | Mrk_14860;29.803 590 | Mrk_16061;30.979 591 | Map_1_255;31.621 592 | Map_1_256;31.633 593 | Mrk_16373;33.163 594 | Map_1_257;33.428 595 | Map_1_258;33.968 596 | Map_1_259;34.525 597 | Mrk_15140;38.087 598 | Map_1_260;42.256 599 | Mrk_16359;43.620 600 | Mrk_16372;43.662 601 | Map_1_261;49.152 602 | Map_1_262;49.154 603 | Map_1_263;49.748 604 | Map_1_264;49.766 605 | Map_1_265;49.814 606 | Map_1_266;49.822 607 | Mrk_16481;51.991 608 | Mrk_16482;52.059 609 | Map_1_267;52.089 610 | Map_1_268;53.541 611 | Map_1_269;53.557 612 | Map_1_270;54.985 613 | Map_1_271;55.129 614 | Map_1_272;57.048 615 | Map_1_273;57.290 616 | Map_1_274;57.290 617 | Map_1_275;57.338 618 | Map_1_276;57.443 619 | Mrk_14962;58.435 620 | Mrk_16599;58.860 621 | Map_1_277;58.990 622 | Mrk_16531;59.849 623 | Map_1_278;61.552 624 | Map_1_279;61.969 625 | Mrk_16727;62.615 626 | Mrk_16598;63.358 627 | Mrk_16597;63.359 628 | Mrk_16524;63.655 629 | Map_1_280;63.655 630 | Mrk_16523;63.676 631 | Map_1_281;67.215 632 | Mrk_16701;71.467 633 | group;LG8 634 | Map_1_283;0.000 635 | Map_1_284;5.486 636 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Mrk_15253;38.766 680 | Map_1_309;39.407 681 | Mrk_14722;39.904 682 | Mrk_17887;40.319 683 | Map_1_310;40.463 684 | Map_1_311;40.481 685 | Mrk_17879;41.064 686 | Mrk_14989;41.423 687 | Mrk_17886;42.273 688 | Mrk_17831;42.752 689 | Map_1_312;42.818 690 | Mrk_17450;43.353 691 | Mrk_17598;43.427 692 | Mrk_17827;44.779 693 | Mrk_17457;45.367 694 | Mrk_17460;45.548 695 | Mrk_16607;48.220 696 | Map_1_313;49.433 697 | Mrk_15773;50.234 698 | Mrk_15704;51.285 699 | Mrk_14776;51.285 700 | Mrk_15702;51.296 701 | Mrk_15724;51.848 702 | Mrk_15728;51.848 703 | Mrk_15455;51.931 704 | Mrk_15529;52.068 705 | Mrk_14787;52.068 706 | Map_1_314;52.232 707 | Mrk_15498;52.444 708 | Map_1_315;53.536 709 | Map_1_316;54.381 710 | Map_1_317;54.384 711 | Mrk_17607;54.796 712 | Mrk_16345;55.054 713 | Mrk_16539;55.058 714 | group;LG9 715 | Map_1_319;0.000 716 | Mrk_17511;0.277 717 | Map_1_320;1.060 718 | Map_1_321;1.111 719 | Mrk_17445;1.185 720 | Map_1_322;1.209 721 | Map_1_323;2.421 722 | Mrk_14933;2.702 723 | Mrk_17963;3.010 724 | Mrk_15236;3.611 725 | Map_1_324;4.228 726 | Mrk_15489;4.615 727 | Map_1_325;4.974 728 | Mrk_15539;4.974 729 | Map_1_326;5.626 730 | Mrk_15815;6.650 731 | Mrk_16038;7.233 732 | Mrk_14977;7.233 733 | Mrk_16259;7.841 734 | Mrk_15825;8.336 735 | Mrk_16009;9.139 736 | Map_1_327;10.237 737 | Mrk_16702;11.368 738 | Mrk_17038;14.273 739 | Mrk_17165;14.439 740 | Mrk_17158;14.442 741 | Mrk_17323;15.628 742 | Mrk_17349;16.141 743 | Map_1_328;16.351 744 | Mrk_17387;16.797 745 | Map_1_329;16.968 746 | Mrk_17388;19.162 747 | Mrk_17485;22.656 748 | Mrk_17565;24.419 749 | Mrk_17609;24.923 750 | Mrk_17673;26.026 751 | Mrk_17677;26.034 752 | Map_1_330;26.042 753 | Mrk_17729;26.043 754 | Map_1_331;26.057 755 | Map_1_332;27.667 756 | Mrk_17771;27.711 757 | Map_1_333;27.962 758 | Mrk_15059;29.278 759 | Map_1_334;29.870 760 | Mrk_17812;29.910 761 | Mrk_17801;30.416 762 | Map_1_335;37.501 763 | Map_1_336;39.169 764 | Mrk_15500;43.157 765 | Map_1_337;43.168 766 | Mrk_15573;44.492 767 | Mrk_14752;44.558 768 | Mrk_15574;44.709 769 | Mrk_15861;47.404 770 | Map_1_338;50.339 771 | Map_1_339;50.353 772 | Map_1_340;52.783 773 | Map_1_341;53.224 774 | Map_1_342;53.245 775 | Mrk_15986;53.549 776 | Map_1_343;53.804 777 | Map_1_344;53.870 778 | Map_1_345;54.336 779 | Map_1_346;54.662 780 | Map_1_347;55.175 781 | Mrk_17074;55.186 782 | Mrk_16101;55.186 783 | Mrk_16104;55.190 784 | Map_1_348;55.198 785 | Map_1_349;55.599 786 | Map_1_350;55.603 787 | Map_1_351;55.603 788 | Mrk_16630;55.671 789 | Mrk_16635;55.674 790 | Mrk_16634;55.674 791 | Mrk_16626;55.680 792 | Mrk_16105;55.681 793 | Map_1_352;55.689 794 | Map_1_353;56.332 795 | Map_1_354;56.332 796 | Map_1_355;56.693 797 | Map_1_356;57.383 798 | Mrk_17080;58.019 799 | Mrk_15160;62.743 800 | Map_1_357;62.749 801 | Mrk_14785;63.938 802 | Mrk_16789;64.512 803 | Mrk_16797;65.101 804 | Mrk_15108;65.686 805 | Mrk_16799;65.687 806 | Map_1_358;66.237 807 | Mrk_16815;66.259 808 | Mrk_16816;66.262 809 | Mrk_16828;67.422 810 | Mrk_16829;68.007 811 | Map_1_359;68.512 812 | Map_1_360;68.578 813 | Mrk_16890;68.592 814 | Mrk_16879;68.603 815 | Mrk_16834;68.608 816 | Mrk_16907;68.621 817 | Map_1_361;68.720 818 | Map_1_362;69.172 819 | Mrk_14910;69.778 820 | Mrk_14753;70.339 821 | group;LG10 822 | Map_1_364;0.000 823 | Map_1_365;0.532 824 | Mrk_16926;1.055 825 | Mrk_17136;2.105 826 | Mrk_17134;2.105 827 | Map_1_366;2.640 828 | Map_1_367;2.640 829 | Mrk_17147;2.982 830 | Map_1_368;4.651 831 | Mrk_17148;5.627 832 | Mrk_17151;5.648 833 | Map_1_369;7.501 834 | Map_1_370;8.166 835 | Mrk_16741;13.489 836 | Mrk_16699;14.138 837 | Mrk_16736;14.138 838 | Mrk_16738;14.480 839 | Map_1_371;14.627 840 | Map_1_372;14.690 841 | Mrk_16685;14.690 842 | Map_1_373;14.700 843 | Mrk_16664;15.728 844 | Map_1_374;16.172 845 | Map_1_375;16.319 846 | Map_1_376;17.057 847 | Map_1_377;19.917 848 | Mrk_16549;20.011 849 | Map_1_378;20.080 850 | Mrk_14732;20.080 851 | Mrk_16582;20.149 852 | Mrk_17090;20.156 853 | Mrk_14921;26.383 854 | Mrk_16427;26.602 855 | Mrk_16430;26.657 856 | Map_1_379;26.670 857 | Map_1_380;26.775 858 | Mrk_16255;27.603 859 | Mrk_16360;28.458 860 | Mrk_16300;28.787 861 | Mrk_16301;28.787 862 | Mrk_16254;29.934 863 | Map_1_381;29.979 864 | Mrk_16431;30.436 865 | Mrk_16361;30.899 866 | Map_1_382;32.687 867 | Map_1_383;33.379 868 | Map_1_384;34.397 869 | Mrk_16164;34.570 870 | Map_1_385;34.570 871 | Map_1_386;35.813 872 | Map_1_387;38.324 873 | Mrk_16048;38.350 874 | Mrk_16047;38.360 875 | Mrk_16003;40.312 876 | Mrk_15833;43.437 877 | Mrk_15951;43.807 878 | Mrk_15126;43.860 879 | Mrk_15948;43.913 880 | Map_1_388;43.988 881 | Mrk_15821;43.999 882 | Map_1_389;45.628 883 | Mrk_15052;45.629 884 | Mrk_15788;45.744 885 | Mrk_15764;46.309 886 | Mrk_15834;46.458 887 | Mrk_15192;46.730 888 | Map_1_390;46.831 889 | Mrk_15748;46.986 890 | Map_1_391;47.246 891 | Map_1_392;48.573 892 | Mrk_15098;49.168 893 | Mrk_15758;49.640 894 | Map_1_393;49.706 895 | Map_1_394;50.125 896 | Mrk_14667;50.233 897 | 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Mrk_15276;65.025 941 | Map_1_420;65.049 942 | Mrk_16386;65.066 943 | Mrk_16382;65.066 944 | Map_1_421;66.214 945 | Mrk_17934;66.783 946 | Mrk_17865;68.491 947 | Mrk_16337;69.795 948 | Map_1_422;70.223 949 | Mrk_16334;76.755 950 | Map_1_423;79.136 951 | Mrk_15492;80.203 952 | Map_1_424;80.228 953 | Map_1_425;80.853 954 | group;LG11 955 | Map_1_427;0.000 956 | Mrk_17267;0.104 957 | Map_1_428;0.667 958 | Map_1_429;1.280 959 | Mrk_17220;2.428 960 | Map_1_430;2.456 961 | Map_1_431;6.550 962 | Mrk_17172;6.557 963 | Mrk_17140;8.913 964 | Map_1_432;8.925 965 | Mrk_17082;10.081 966 | Mrk_17084;10.124 967 | Mrk_17085;10.163 968 | Map_1_433;10.281 969 | Mrk_17070;10.742 970 | Mrk_17069;11.338 971 | Map_1_434;12.519 972 | Mrk_16962;14.297 973 | Map_1_435;26.261 974 | Mrk_17254;26.261 975 | Map_1_436;26.825 976 | Map_1_437;28.695 977 | Mrk_17217;29.314 978 | Mrk_16639;30.058 979 | Mrk_16633;30.353 980 | Mrk_16604;30.687 981 | Mrk_17202;32.187 982 | Mrk_16571;34.353 983 | Mrk_17139;35.334 984 | 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Mrk_14831;106.371 1486 | group;LG16 1487 | Map_1_637;0.000 1488 | Map_1_638;0.052 1489 | Mrk_17712;1.127 1490 | Map_1_639;1.130 1491 | Map_1_640;1.718 1492 | Map_1_641;2.742 1493 | Mrk_17765;4.282 1494 | Mrk_15418;5.616 1495 | Mrk_15425;5.655 1496 | Mrk_14984;5.932 1497 | Map_1_642;6.815 1498 | Mrk_15720;7.455 1499 | Mrk_15699;7.455 1500 | Mrk_15673;7.799 1501 | Map_1_643;8.086 1502 | Mrk_15759;8.755 1503 | Map_1_644;9.098 1504 | Map_1_645;10.523 1505 | Map_1_646;11.837 1506 | Mrk_15939;11.837 1507 | Mrk_14836;12.727 1508 | Map_1_647;16.406 1509 | Map_1_648;16.564 1510 | Map_1_649;16.567 1511 | Mrk_14759;16.598 1512 | Mrk_16717;16.627 1513 | Map_1_650;16.643 1514 | Map_1_651;16.742 1515 | Mrk_16943;16.835 1516 | Mrk_16996;16.835 1517 | Mrk_16999;16.944 1518 | Mrk_16715;16.944 1519 | Map_1_652;18.092 1520 | Mrk_17160;18.239 1521 | Mrk_17199;18.407 1522 | Mrk_17177;18.407 1523 | Mrk_17208;18.722 1524 | Mrk_17166;19.196 1525 | Mrk_17258;19.426 1526 | Mrk_17256;19.441 1527 | Map_1_653;19.441 1528 | Mrk_17255;19.562 1529 | Mrk_14907;19.771 1530 | Mrk_17295;20.517 1531 | Mrk_17318;20.777 1532 | Mrk_17317;20.777 1533 | Mrk_14741;21.910 1534 | Map_1_654;21.910 1535 | Map_1_655;23.772 1536 | Mrk_17495;24.206 1537 | Mrk_17493;24.349 1538 | Mrk_17513;25.492 1539 | Mrk_15061;27.174 1540 | Mrk_17579;27.232 1541 | Map_1_656;27.270 1542 | Mrk_14884;27.555 1543 | Mrk_17663;27.861 1544 | Map_1_657;29.206 1545 | Mrk_17641;29.271 1546 | Mrk_17645;29.296 1547 | Map_1_658;29.438 1548 | Map_1_659;29.541 1549 | Map_1_660;29.834 1550 | Map_1_661;29.921 1551 | Map_1_662;30.061 1552 | Map_1_663;30.838 1553 | Map_1_664;31.128 1554 | Map_1_665;31.644 1555 | Map_1_666;32.882 1556 | Map_1_667;34.042 1557 | Map_1_668;34.243 1558 | Map_1_669;34.523 1559 | Map_1_670;34.524 1560 | Map_1_671;34.649 1561 | Map_1_672;35.925 1562 | Map_1_673;35.928 1563 | Map_1_674;35.928 1564 | Mrk_17817;36.556 1565 | Mrk_17828;36.885 1566 | Mrk_17823;36.885 1567 | Mrk_17898;37.710 1568 | Map_1_675;37.968 1569 | Mrk_17903;38.029 1570 | Mrk_17905;38.029 1571 | Mrk_17907;38.552 1572 | Map_1_676;39.454 1573 | Mrk_17914;39.828 1574 | Map_1_677;40.294 1575 | Mrk_15014;40.569 1576 | Mrk_15437;41.468 1577 | Mrk_15432;41.651 1578 | Mrk_15443;41.813 1579 | Mrk_15493;42.080 1580 | Mrk_15113;42.088 1581 | Mrk_14954;43.850 1582 | Mrk_15568;45.014 1583 | Map_1_678;45.020 1584 | Mrk_14993;45.700 1585 | Map_1_679;47.259 1586 | Mrk_15621;48.347 1587 | Mrk_14761;48.348 1588 | Mrk_15651;48.930 1589 | Mrk_15118;49.454 1590 | Mrk_15674;50.066 1591 | Mrk_15677;50.067 1592 | Mrk_15676;50.068 1593 | Mrk_15683;50.069 1594 | Mrk_15710;51.757 1595 | Mrk_15187;51.763 1596 | Map_1_680;52.237 1597 | Map_1_681;52.310 1598 | Map_1_682;52.371 1599 | Map_1_683;52.784 1600 | Map_1_684;53.191 1601 | Mrk_17953;53.464 1602 | Mrk_15128;53.981 1603 | Map_1_685;54.210 1604 | Mrk_16010;54.737 1605 | Map_1_686;54.737 1606 | Mrk_14917;54.751 1607 | Mrk_15234;55.415 1608 | Map_1_687;55.431 1609 | Mrk_17981;55.581 1610 | Mrk_17967;55.600 1611 | Mrk_14814;55.987 1612 | Map_1_688;58.315 1613 | Map_1_689;58.695 1614 | Map_1_690;59.374 1615 | Map_1_691;59.491 1616 | Map_1_692;59.735 1617 | Map_1_693;60.167 1618 | Mrk_15883;60.613 1619 | Mrk_16216;60.613 1620 | Map_1_694;60.635 1621 | Map_1_695;60.636 1622 | Map_1_696;60.660 1623 | Map_1_697;61.112 1624 | Map_1_698;61.137 1625 | group;LG17 1626 | Map_1_700;0.000 1627 | Map_1_701;0.004 1628 | Map_1_702;3.482 1629 | Map_1_703;3.602 1630 | Map_1_704;3.694 1631 | Map_1_705;4.152 1632 | Map_1_706;4.234 1633 | Mrk_16370;4.239 1634 | Mrk_16374;4.289 1635 | Map_1_707;4.834 1636 | Map_1_708;4.865 1637 | Mrk_16331;4.885 1638 | Mrk_16125;5.438 1639 | Mrk_16124;5.454 1640 | Map_1_709;6.637 1641 | Map_1_710;6.637 1642 | Map_1_711;7.226 1643 | Mrk_16059;7.810 1644 | Map_1_712;7.814 1645 | Map_1_713;9.019 1646 | Mrk_15997;9.591 1647 | Mrk_15994;10.257 1648 | Map_1_714;10.782 1649 | Map_1_715;11.781 1650 | Map_1_716;11.991 1651 | Map_1_717;12.093 1652 | Map_1_718;12.159 1653 | Map_1_719;16.096 1654 | Map_1_720;17.285 1655 | Map_1_721;17.285 1656 | Map_1_722;19.051 1657 | Mrk_15476;23.922 1658 | Mrk_14829;25.145 1659 | Map_1_723;25.214 1660 | Map_1_724;25.411 1661 | Map_1_725;25.684 1662 | Map_1_726;28.099 1663 | Mrk_15246;28.126 1664 | Map_1_727;28.126 1665 | Map_1_728;28.133 1666 | Mrk_14889;28.222 1667 | Mrk_17857;31.872 1668 | Map_1_729;32.940 1669 | Mrk_17933;33.486 1670 | Map_1_730;34.484 1671 | Map_1_731;34.484 1672 | Mrk_17750;36.577 1673 | Mrk_17685;37.410 1674 | Map_1_732;38.187 1675 | Mrk_17402;40.314 1676 | Mrk_14806;41.374 1677 | Mrk_14982;41.471 1678 | Mrk_17008;41.471 1679 | Mrk_17525;41.518 1680 | Map_1_733;41.636 1681 | Mrk_16748;42.863 1682 | Mrk_17222;44.245 1683 | Mrk_16412;44.387 1684 | Map_1_734;44.424 1685 | Mrk_17224;44.424 1686 | Map_1_735;44.433 1687 | Mrk_17039;44.953 1688 | Mrk_16743;44.963 1689 | Mrk_16281;44.982 1690 | Mrk_16240;45.557 1691 | Map_1_736;46.577 1692 | Map_1_737;46.577 1693 | Map_1_738;47.308 1694 | Mrk_16040;47.661 1695 | Map_1_739;47.768 1696 | Map_1_740;48.236 1697 | Mrk_15538;48.254 1698 | Map_1_741;48.254 1699 | Map_1_742;48.258 1700 | Mrk_14911;48.261 1701 | Mrk_15707;48.261 1702 | Mrk_15534;48.263 1703 | Map_1_743;48.298 1704 | Mrk_15326;49.912 1705 | Map_1_744;51.200 1706 | Mrk_17800;51.322 1707 | Mrk_17792;52.388 1708 | Mrk_15092;53.435 1709 | Mrk_17702;53.471 1710 | Mrk_17294;53.567 1711 | Mrk_16972;54.415 1712 | Mrk_14684;54.708 -------------------------------------------------------------------------------- /DATA/EX_HELP_PAGE/Example_Data_Set1.csv: -------------------------------------------------------------------------------- 1 | LG;marker;position LG1;marker_52;0 LG1;marker_23;29.4 LG1;marker_18;31.2 LG1;marker_8;40.5 LG2;marker_12;0 LG2;marker_3;3.3 LG2;marker_98;4.6 LG2;marker_72;10.8 -------------------------------------------------------------------------------- /DATA/EX_HELP_PAGE/Example_Data_Set2.csv: -------------------------------------------------------------------------------- 1 | group;LG1 Map_1_1;0.000 Map_1_2;1.275 Map_1_3;1.412 Mrk_17036;3.743 Map_1_4;4.765 Mrk_16976;5.038 Map_1_5;9.671 Mrk_16756;12.513 Mrk_15204;12.683 Map_1_6;14.325 Map_1_7;14.425 Map_1_8;14.427 Map_1_9;14.429 -------------------------------------------------------------------------------- /DATA/EX_HELP_PAGE/Example_Data_Set3.txt: -------------------------------------------------------------------------------- 1 | 0 -801.56 {1 -485.24 MS4 0.0 MS5 3.3 MS13 38.8 MS6 64.2 MS11 86.4 MS17 137.9 MS16 159.5 MS8 186.5 MS7 192.4 MS2 207.4 MS3 208.0 MS9 231.5 MS15 249.3 MS12 252.8 MS20 291.1 MS19 293.8 MS1 483.9} {2 -316.32 MS4 0.0 MS5 84.4 MS6 138.5 MS8 229.0 MS7 307.2 MS3 493.5 MS9 706.1 MS15 862.8 MS1 1622.9 G36 1726.5 G39 1786.2 G37 1845.9 G40 1871.3} -------------------------------------------------------------------------------- /DATA/EX_HELP_PAGE/titi.txt: -------------------------------------------------------------------------------- 1 | yo 2 | yo -------------------------------------------------------------------------------- /DATA/EX_HELP_PAGE/toto.txt: -------------------------------------------------------------------------------- 1 | 0 -801.56 {1 -485.24 MS4 0.0 MS5 3.3 MS13 38.8 MS6 64.2 MS11 86.4 MS17 137.9 MS16 159.5 MS8 186.5 MS7 192.4 MS2 207.4 MS3 208.0 MS9 231.5 MS15 249.3 MS12 252.8 MS20 291.1 MS19 293.8 MS1 483.9} {2 -316.32 MS4 0.0 MS5 84.4 MS6 138.5 MS8 229.0 MS7 307.2 MS3 493.5 MS9 706.1 MS15 862.8 MS1 1622.9 G36 1726.5 G39 1786.2 G37 1845.9 G40 1871.3} -------------------------------------------------------------------------------- /DATA/SMALL_DATASET/titi: -------------------------------------------------------------------------------- 1 | group marker position 2 | 1A Cluster_3354|Contig2|original@283 0 3 | 1A Cluster_9277|Contig1|likelySeq@519 6.09999999999999 4 | 1A Cluster_9277|Contig1|likelySeq@549 6.09999999999999 5 | 1A Cluster_10029|Contig1|original@626 7 6 | 1A Cluster_10029|Contig1|original@668 7 7 | 1A Cluster_16263|Contig2|original@713 7 8 | 1A Cluster_9277|Contig2|original@99 7 9 | 1A Cluster_10305|Contig1|complementarySeq@190 8.5 10 | 1A Cluster_2150|Contig5|original@1711 8.5 11 | 1A Cluster_1882|Contig4|original@2077 9.59999999999999 12 | 1A Cluster_1386|Contig2|original@858 10.7 13 | 1A Cluster_1882|Contig10|original@1836 10.7 14 | 1A Cluster_1973|Contig2|original@1480 10.7 15 | 1A Cluster_10103|Contig1|original@140 11.4 16 | 1A Cluster_1882|Contig10|original@1818 11.4 17 | 1A Cluster_1882|Contig4|original@2428 11.4 18 | 1A Cluster_1882|Contig6|original@1226 11.4 19 | 1A Cluster_1882|Contig8|original@1158 11.4 20 | 1A Cluster_21164|Contig1|original@399 11.4 21 | -------------------------------------------------------------------------------- /DATA/SMALL_DATASET/tktk: -------------------------------------------------------------------------------- 1 | group marker position 2 | 1A Cluster_3354|Contig2|original@283 0 3 | 1A cluster_singlet|EPO_092_2826919364122958018+,...,87210-|original@654 0 4 | 1A Cluster_10322|Contig1|complementarySeq@393 0.7 5 | 1A Cluster_3335|Contig1|complementarySeq@1206 0.7 6 | 1A Cluster_3335|Contig1|likelySeq@1206 0.7 7 | 1A Cluster_5949|Contig4|original@2220 0.7 8 | 1A Cluster_7725|Contig5|original@540 0.7 9 | 1A Cluster_1386|Contig2|original@880 1.3 10 | 1A Cluster_6015|Contig5|original@225 7.6 11 | 1A Cluster_6015|Contig8|original@230 7.6 12 | 1A Cluster_7051|Contig4|original@1363 7.6 13 | 1A Cluster_10103|Contig1|original@211 8.9 14 | 1A Cluster_2150|Contig2|original@1760 9.3 15 | 1A Cluster_2150|Contig3|original@1573 9.3 16 | 1A Cluster_2150|Contig5|original@1711 9.3 17 | 1A Cluster_10029|Contig1|original@800 13.2 18 | 1A Cluster_9277|Contig1|likelySeq@519 13.2 19 | 1A Cluster_9277|Contig2|original@99 13.2 20 | 1A Cluster_9687|Contig1|original@1017 13.7 21 | 1A Cluster_10014|Contig7|original@280 15.9 22 | 1A Cluster_11076|Contig1|original@251 17.6 23 | 1A Cluster_11076|Contig1|original@547 17.6 24 | 1A Cluster_11076|Contig1|original@868 17.6 25 | 1A Cluster_14274|Contig1|original@346 18.6 26 | 1A Cluster_8411|Contig1|original@146 18.6 27 | 1A Cluster_297|Contig37|complementarySeq@365 40.7 28 | 1A Cluster_4373|Contig2|original@910 40.7 29 | 1A Cluster_4946|Contig1|original@164 40.7 30 | 1A Cluster_6679|Contig1|original@278 40.7 31 | -------------------------------------------------------------------------------- /DATA/SMALL_DATASET/toto: -------------------------------------------------------------------------------- 1 | group marker position 2 | 1A Cluster_10322|Contig1|complementarySeq@393 0 3 | 1A Cluster_5949|Contig4|original@2220 0.4 4 | 1A Cluster_7725|Contig5|original@540 0.4 5 | 1A Cluster_1386|Contig2|original@880 0.8 6 | 1A Cluster_16263|Contig2|original@713 19.6 7 | 1A Cluster_9687|Contig1|original@1470 19.6 8 | 1A Cluster_10029|Contig1|original@626 19.6 9 | 1A Cluster_11076|Contig1|original@547 24.5 10 | 1A Cluster_11076|Contig1|original@251 24.5 11 | 1A Cluster_11076|Contig1|original@868 24.5 12 | 1A Cluster_8411|Contig1|original@146 25 13 | 1A Cluster_6179|Contig1|original@815 47.5 14 | 1A Cluster_9630|Contig1|likelySeq@1992 47.9 15 | 1A Cluster_9947|Contig2|original@442 47.9 16 | 1A Cluster_677|Contig6|original@814 48.1 17 | 1A Cluster_5602|Contig2|original@450 48.1 18 | 1A Cluster_677|Contig3|original@1738 48.1 19 | 1A Cluster_5602|Contig2|original@984 48.82 20 | 1A Cluster_2587|Contig1|original@394 49.1 21 | -------------------------------------------------------------------------------- /DATA/SMALL_DATASET/tutu: -------------------------------------------------------------------------------- 1 | group marker position 2 | 1A Cluster_3354|Contig2|original@283 0 3 | 1A cluster_singlet|EPO_092_2826919364122958018+,...,87210-|original@654 0 4 | 1A Cluster_10322|Contig1|complementarySeq@393 0.7 5 | 1A Cluster_3335|Contig1|complementarySeq@1206 0.7 6 | 1A Cluster_3335|Contig1|likelySeq@1206 0.7 7 | 1A Cluster_5949|Contig4|original@2220 0.7 8 | 1A Cluster_7725|Contig5|original@540 0.7 9 | 1A Cluster_1386|Contig2|original@880 1.3 10 | 1A Cluster_6015|Contig5|original@225 7.6 11 | 1A Cluster_6015|Contig8|original@230 7.6 12 | 1A Cluster_7051|Contig4|original@1363 7.6 13 | 1A Cluster_10103|Contig1|original@211 8.9 14 | 1A Cluster_2150|Contig2|original@1760 9.3 15 | 1A Cluster_2150|Contig3|original@1573 9.3 16 | 1A Cluster_2150|Contig5|original@1711 9.3 17 | 1A Cluster_10029|Contig1|original@800 13.2 18 | 1A Cluster_9277|Contig1|likelySeq@519 13.2 19 | 1A Cluster_9277|Contig2|original@99 13.2 20 | 1A Cluster_9687|Contig1|original@1017 13.7 21 | -------------------------------------------------------------------------------- /DATA/SMALL_DATASET/tyty: -------------------------------------------------------------------------------- 1 | group marker position 2 | 1A Cluster_10322|Contig1|complementarySeq@393 0 3 | 1A Cluster_5949|Contig4|original@2220 0.4 4 | 1A Cluster_7725|Contig5|original@540 0.4 5 | 1A Cluster_1386|Contig2|original@880 0.8 6 | 1A Cluster_16263|Contig2|original@713 19.6 7 | 1A Cluster_9687|Contig1|original@1470 19.6 8 | 1A Cluster_10029|Contig1|original@626 19.6 9 | 1A Cluster_11076|Contig1|original@547 24.5 10 | 1A Cluster_11076|Contig1|original@251 24.5 11 | 1A Cluster_11076|Contig1|original@868 24.5 12 | 1A Cluster_8411|Contig1|original@146 25 13 | 1A Cluster_6179|Contig1|original@815 47.5 14 | 1A Cluster_9630|Contig1|likelySeq@1992 47.9 15 | 1A Cluster_9947|Contig2|original@442 47.9 16 | 1A Cluster_677|Contig6|original@814 48.1 17 | 1A Cluster_5602|Contig2|original@450 48.1 18 | 1A Cluster_677|Contig3|original@1738 48.1 19 | 1A Cluster_5602|Contig2|original@984 48.82 20 | 1A Cluster_2587|Contig1|original@394 49.1 21 | 1A Cluster_5099|Contig2|original@689 49.66 22 | 1A Cluster_9|Contig6|original@329 49.66 23 | 1A Cluster_31|Contig1|original@914 51.7 24 | 1A Cluster_4529|Contig2|original@487 52.5 25 | 1A Cluster_369|Contig8|original@2061 53.8 26 | 1A Cluster_31|Contig2|original@448 53.8 27 | 1A Cluster_9272|Contig2|original@86 55.9 28 | 1A Cluster_117|Contig9|original@2312 56.875 29 | 1A Cluster_31|Contig8|original@906 58.1454545454545 30 | 1A Cluster_13257|Contig1|original@341 58.4 31 | -------------------------------------------------------------------------------- /DATA/SORGHUM/CIRAD: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 sPb-1040 0 3 | 1 M188427 1.3 4 | 1 umc84 3.6 5 | 1 sPb-8325 4.6 6 | 1 sPb-7161 8.7 7 | 1 sPb-5521 8.7 8 | 1 sPb-0623 8.7 9 | 1 sPb-2583 8.7 10 | 1 txp208 8.7 11 | 1 SbRPG837 9.4 12 | 1 msbcir268 11.3 13 | 1 HVHGA1 14.2 14 | 1 HVGAD1 14.2 15 | 1 umc107 14.2 16 | 1 msbcir347 14.2 17 | 1 txp302 14.2 18 | 1 sPb-3891 14.2 19 | 1 msbcir242 14.2 20 | 1 msbcir228 16 21 | 1 M340904 18.5 22 | 1 M342631 20.8 23 | 1 sbAGFO8 23.5 24 | 1 umc140 24.2 25 | 1 sPb-6917 25.6 26 | 1 sPb-6577 35.4 27 | 1 sPb-4329 41 28 | 1 SbRPG873 41.3 29 | 1 umc27 41.3 30 | 1 M189295 47.5 31 | 1 sPb-5315 48.7 32 | 1 M340898 49.9 33 | 1 sPb-8041 50.5 34 | 1 sPb-6176 58.2 35 | 1 txp182 60 36 | 1 sPb-3618 60 37 | 1 umc83 63.4 38 | 1 sPb-4793 66.6 39 | 1 gpsb089 71.4 40 | 1 sPb-3727 73.7 41 | 1 sPb-1191 74.4 42 | 1 sPb-3525 74.8 43 | 1 sPb-4234 75.2 44 | 1 M187880 76.4 45 | 1 M187200 78.8 46 | 1 sPb-2684 82.1 47 | 1 sPb-2412 82.1 48 | 1 sPb-9216 82.1 49 | 1 sPb-0810 82.4 50 | 1 M188404 84.2 51 | 1 txp43 84.8 52 | 1 M343014 84.8 53 | 1 txp88 85.1 54 | 1 sPb-0232 88.6 55 | 1 M343356 89.3 56 | 1 sPb-5358 91.9 57 | 1 sPb-2602 98.3 58 | 1 sPb-6600 100.4 59 | 1 sPb-7567 101.5 60 | 1 sPb-8492 102.2 61 | 1 M188582 107.5 62 | 1 M189251 107.5 63 | 1 msbcir304b 112.4 64 | 1 sPb-3311 112.4 65 | 1 msbcir286 115.5 66 | 1 gpsb173 115.5 67 | 1 sPb-3801 122.3 68 | 1 gap57 125.2 69 | 1 sPb-8423 125.2 70 | 1 sPb-7958 125.6 71 | 1 sPb-6083 126 72 | 1 gpsb051 126 73 | 1 txp75 127.4 74 | 1 M187238 128.6 75 | 1 M343241 131.6 76 | 1 M187616 131.6 77 | 1 gap36 132.9 78 | 1 msbcir336 134.2 79 | 1 bnl5.09 137.4 80 | 1 sPb-0233 142.1 81 | 1 M343279 146.2 82 | 1 SbRPG80 149.1 83 | 1 txp340 153.5 84 | 1 M342904 153.5 85 | 1 M340364 154.2 86 | 1 msbcir306 158.3 87 | 1 sPb-7611 158.3 88 | 1 sPb-4435 160.1 89 | 1 gpsb129 163 90 | 1 M340373 163 91 | 1 sPb-8743 166.3 92 | 2 sPb-7549 0 93 | 2 sPb-7657 0 94 | 2 sPb-4855 0 95 | 2 M200042 1.8 96 | 2 M341662 1.8 97 | 2 M341592 2.4 98 | 2 msbcir266 2.4 99 | 2 sPb-2754 4.4 100 | 2 gpsb011 4.4 101 | 2 gpsb174 4.4 102 | 2 M187772 6.7 103 | 2 sPb-0333 6.7 104 | 2 sPb-3103 7.7 105 | 2 txp25 9.9 106 | 2 gpsb178 9.9 107 | 2 M342927 9.9 108 | 2 M342711 9.9 109 | 2 sPb-8345 9.9 110 | 2 M340957 10.6 111 | 2 M341741 11 112 | 2 msbcir223 13.4 113 | 2 sPb-7668 13.4 114 | 2 sPb-7668 13.4 115 | 2 txp211 15.2 116 | 2 OPA7 15.5 117 | 2 sPb-3086 15.5 118 | 2 sPb-1764 15.5 119 | 2 sPb-9687 15.5 120 | 2 M188881 17.3 121 | 2 M343289 17.3 122 | 2 sPb-1127 17.3 123 | 2 M342329 20.9 124 | 2 M340416 20.9 125 | 2 M342090 20.9 126 | 2 SbRPG136 20.9 127 | 2 sPb-3798 21.5 128 | 2 sPb-5347 30.2 129 | 2 sPb-8376 30.2 130 | 2 M342380 32 131 | 2 sPb-7177 32 132 | 2 sPb-8897 32 133 | 2 sPb-5643 32 134 | 2 umc55 36.2 135 | 2 sPb-0364 40.1 136 | 2 sPb-5224 40.1 137 | 2 sPb-4081 42.3 138 | 2 msbcir238 43.6 139 | 2 sPb-8616 43.6 140 | 2 sPb-4419 43.6 141 | 2 gpsb087 44.8 142 | 2 M187382 47 143 | 2 M340431 47 144 | 2 M342471 47 145 | 2 sPb-7695 47 146 | 2 sPb-1920 47.3 147 | 2 sPb-6434 47.6 148 | 2 sPb-6288 47.6 149 | 2 sPb-4970 47.9 150 | 2 M340888 50.5 151 | 2 M343120 50.9 152 | 2 M343067 50.9 153 | 2 sPb-7882 54 154 | 2 M343086 57.8 155 | 2 M188424 57.8 156 | 2 M342358 57.8 157 | 2 M188643 57.8 158 | 2 M188479 58.6 159 | 2 M342759 58.6 160 | 2 M342673 58.6 161 | 2 sPb-0350 58.6 162 | 2 M340532 60 163 | 2 M342498 60 164 | 2 sbAGAB03 66.3 165 | 2 msbcir312 66.3 166 | 2 M341108 71.5 167 | 2 M188174 71.5 168 | 2 M187697 71.5 169 | 2 M188348 71.5 170 | 2 sPb-1970 74.1 171 | 2 M187871 75 172 | 2 M187894 75 173 | 2 M188335 78.8 174 | 2 msbcir339 86.2 175 | 2 M189020 86.2 176 | 2 txp1 88.8 177 | 2 M342762 91.4 178 | 2 sPb-3917 92.8 179 | 2 sPb-2568 96.8 180 | 2 sPb-3361 99.3 181 | 2 M340414 99.3 182 | 2 M342409 99.3 183 | 2 M340589 99.3 184 | 2 sPb-4862 99.6 185 | 2 sPb-2229 100.2 186 | 2 sPb-6424 102.2 187 | 2 sPb-1925 102.8 188 | 3 sPb-8706 0 189 | 3 SSmsbcir78 1.3 190 | 3 sPb-8568 3 191 | 3 sPb-0363 4.3 192 | 3 M342700 6 193 | 3 M189270 6 194 | 3 sPb-5454 6.4 195 | 3 M199861 6.4 196 | 3 M340963 6.7 197 | 3 M340747 9.1 198 | 3 M342200 9.1 199 | 3 M188917 12.9 200 | 3 M342283 12.9 201 | 3 gpsb012 15.5 202 | 3 sPb-2309 15.5 203 | 3 sPb-6714 15.5 204 | 3 sPb-2461 15.5 205 | 3 sPb-5594 15.5 206 | 3 M341408 15.5 207 | 3 umc124 15.8 208 | 3 M342808 15.8 209 | 3 sPb-1906 16.5 210 | 3 sPb-4915 16.5 211 | 3 sPb-6982 16.5 212 | 3 sPb-2055 17.5 213 | 3 sPb-1669 17.5 214 | 3 sPb-0656 17.5 215 | 3 M188933 17.5 216 | 3 M188681 18.1 217 | 3 M340763 18.1 218 | 3 M188657 18.1 219 | 3 M341540 18.1 220 | 3 SBRPG846 19.1 221 | 3 M342547 20.2 222 | 3 sPb-0319 22 223 | 3 M341223 24.4 224 | 3 sPb-5409 25 225 | 3 txp215 25 226 | 3 sPb-9135 25.8 227 | 3 M187276 28.4 228 | 3 sPb-6857 29.9 229 | 3 M340732 36.9 230 | 3 sPb-9388 37.5 231 | 3 SbRPG106 37.5 232 | 3 sPb-4226 45.1 233 | 3 gpsb086 45.1 234 | 3 sPb-2562 51.5 235 | 3 sPb-9076 64.5 236 | 3 sPb-7688 72.4 237 | 3 sPb-5108 73.4 238 | 3 sPb-1362 73.4 239 | 3 sPb-6122 73.7 240 | 3 sPb-1137 75.2 241 | 3 msbcir224 77.2 242 | 3 txp33 77.2 243 | 3 gap236 80.3 244 | 3 sPb-9779 80.3 245 | 3 M187546 80.3 246 | 3 sPb-5922 82.1 247 | 3 csu58 85 248 | 3 M340384 85 249 | 3 M341885 85 250 | 3 M340852 85 251 | 3 M342663 85 252 | 3 M340802 85 253 | 3 M188114 85 254 | 3 txp31 85.7 255 | 3 msbcir276 88.3 256 | 3 sPb-2209 95.9 257 | 3 sPb-3965 101.5 258 | 3 sPb-1496 102.1 259 | 3 sPb-9139 106.1 260 | 3 msbcir314 106.1 261 | 3 sPb-1725 108.3 262 | 3 sbAGEO1 110.2 263 | 3 sPb-1480 110.2 264 | 3 M341446 117 265 | 3 M341571 117 266 | 3 M188311 123.2 267 | 3 msbcir225 123.2 268 | 3 txp38 127.7 269 | 3 M188846 127.7 270 | 3 M342704 127.7 271 | 3 sPb-6013 127.7 272 | 3 txp34 130.5 273 | 3 bnl5.37 131.9 274 | 3 sPb-3228 131.9 275 | 3 sPb-6925 131.9 276 | 3 M340973 133.6 277 | 3 M342779 133.6 278 | 3 M342567 135 279 | 3 M188347 135 280 | 3 M188920 135 281 | 3 M342305 135 282 | 3 sPb-9894 137.2 283 | 3 sPb-6770 141.4 284 | 4 sPb-4074 0 285 | 4 sPb-7493 0 286 | 4 sPb-8446 1 287 | 4 sPb-2739 1 288 | 4 msbcir340 2.3 289 | 4 sPb-4718 2.9 290 | 4 sPb-4233 6 291 | 4 sPb-7042 6 292 | 4 gpsb050 9.2 293 | 4 sPb-1853 15.8 294 | 4 msbcir323 15.8 295 | 4 M187763 15.8 296 | 4 sPb-1932 18.7 297 | 4 M187985 18.7 298 | 4 M188323 21.9 299 | 4 M200413 21.9 300 | 4 RZ599 26.7 301 | 4 M188005 27.7 302 | 4 M188903 27.7 303 | 4 sPb-2944 30.5 304 | 4 sPb-6547 30.5 305 | 4 sPb-2842 34.7 306 | 4 M340930 36.2 307 | 4 M187716 43.6 308 | 4 M187502 45 309 | 4 sPb-8488 46.8 310 | 4 M343007 49.2 311 | 4 sPb-7534 49.2 312 | 4 txp12 50.5 313 | 4 M187661 52.5 314 | 4 sbAGGO2 64.3 315 | 4 RZ166 64.7 316 | 4 M340886 64.7 317 | 4 M341851 64.7 318 | 4 M340482 64.7 319 | 4 M340990 64.7 320 | 4 sPb-2084 64.7 321 | 4 sPb-0854 64.7 322 | 4 sPb-9041 64.7 323 | 4 M200347 68.9 324 | 4 umc68 84 325 | 4 sPb-4649 84 326 | 4 M340664 85.3 327 | 4 M342464 85.3 328 | 4 M342470 85.6 329 | 4 M187539 85.6 330 | 4 gpsb073 85.6 331 | 4 txp24 87.3 332 | 4 M340375 89.7 333 | 4 M341949 89.7 334 | 4 csu26 89.7 335 | 4 M342928 92.2 336 | 4 M342010 92.2 337 | 4 M342065 93.4 338 | 4 txp327 93.4 339 | 4 txp60 93.4 340 | 4 msbcir232 95.6 341 | 4 sPb-8100 96.8 342 | 4 M187223 96.8 343 | 4 M340952 96.8 344 | 4 M187170 96.8 345 | 4 M187207 96.8 346 | 4 M188305 96.8 347 | 4 M342916 96.8 348 | 4 M188710 99.7 349 | 4 gpsb045 105.7 350 | 4 M342320 113.6 351 | 4 M342191 113.6 352 | 4 M341902 113.6 353 | 4 M342643 113.6 354 | 4 M187461 113.6 355 | 4 M187118 113.6 356 | 4 sPb-4692 114.9 357 | 4 M340433 114.9 358 | 4 gpsb028 117.9 359 | 4 txp21 119.4 360 | 4 sPb-0110 119.8 361 | 4 M341593 119.8 362 | 4 M341500 120.5 363 | 5 msbcir329 0 364 | 5 M188451 0 365 | 5 gpsb159 1.3 366 | 5 M341693 2 367 | 5 sPb-4041 2 368 | 5 sPb-0906 2.9 369 | 5 txp65 8.3 370 | 5 sPb-0381 8.3 371 | 5 sPb-7047 11 372 | 5 sPb-5256 11 373 | 5 gpsb84 15.5 374 | 5 M188102 15.5 375 | 5 M340916 15.5 376 | 5 sPb-5692 15.5 377 | 5 gpsb005 16.3 378 | 5 M342865 16.3 379 | 5 sPb-4131 16.3 380 | 5 sPb-3977 18.1 381 | 5 sPb-5317 18.1 382 | 5 sPb-1282 18.1 383 | 5 M188898 18.1 384 | 5 msbcir248 18.1 385 | 5 sPb-5390 18.1 386 | 5 sPb-8247 20.1 387 | 5 sPb-0510 20.1 388 | 5 umc8 20.1 389 | 5 gpsb017 20.1 390 | 5 gpsb165 20.1 391 | 5 sPb-5408 20.1 392 | 5 sPb-9080 20.1 393 | 5 sPb-3995 25.2 394 | 5 sPb-0932 40.1 395 | 5 sPb-1989 52 396 | 5 M187365 54.3 397 | 5 M340444 60.3 398 | 5 M340397 60.3 399 | 5 sPb-6787 60.3 400 | 5 M340953 60.3 401 | 5 sPb-5399 60.3 402 | 5 M189058 60.3 403 | 5 sPb-6861 60.3 404 | 5 txp15 61.2 405 | 5 M340502 61.2 406 | 5 M340464 61.2 407 | 5 M341521 61.2 408 | 5 sPb-6341 62.6 409 | 5 M188512 63.3 410 | 5 M341155 64.4 411 | 5 sPb-7893 64.4 412 | 5 sPb-9312 64.4 413 | 5 sPb-2187 64.4 414 | 5 M341517 64.4 415 | 5 sPb-8470 64.4 416 | 5 M188205 64.7 417 | 5 sPb-9054 64.7 418 | 5 M343065 68.8 419 | 5 M188878 69.4 420 | 5 sPb-4086 69.4 421 | 5 M342398 69.4 422 | 5 csu166 70.8 423 | 5 sPb-6855 72.8 424 | 5 sPb-7106 73.8 425 | 5 gpsb003 77.9 426 | 5 txp23 80.2 427 | 5 M188834 85.5 428 | 5 gpsb176 88.3 429 | 5 SbRPG608 94.9 430 | 5 sPb-9490 98.8 431 | 5 sbKAFGK1 102.5 432 | 5 sPb-2570 102.5 433 | 5 sPb-9858 102.5 434 | 5 sPb-1181 105.6 435 | 5 sPb-2161 109.3 436 | 5 M342033 109.3 437 | 5 M343312 109.3 438 | 5 M341274 109.3 439 | 5 M342046 109.3 440 | 5 M340547 109.3 441 | 5 sPb-3418 111.2 442 | 5 sPb-5562 111.2 443 | 5 sPb-7892 111.2 444 | 5 msbcir222 111.9 445 | 5 SSmsbcir120 114 446 | 5 gpsb032 118.3 447 | 6 sPb-1677 0 448 | 6 sPb-9772 0 449 | 6 sPb-1491 0 450 | 6 msbcir211 5.4 451 | 6 M342640 5.4 452 | 6 sPb-8793 9 453 | 6 sPb-3644 9 454 | 6 cdo456 9.7 455 | 6 sPb-9511 22.5 456 | 6 sPb-8963 25 457 | 6 txp6 26.5 458 | 6 M340448 31.5 459 | 6 M187805 34.8 460 | 6 sPb-8362 34.8 461 | 6 sPb-5564 34.8 462 | 6 sPb-6017 34.8 463 | 6 M342664 34.8 464 | 6 gap7 40.3 465 | 6 gap72 40.3 466 | 6 sPb-5506 40.3 467 | 6 sPb-7660 40.3 468 | 6 M187589 40.3 469 | 6 M342089 43.1 470 | 6 M340758 43.1 471 | 6 SSmsbcir92 43.1 472 | 6 M341530 43.1 473 | 6 sPb-4947 45.5 474 | 6 sPb-4503 47.1 475 | 6 sPb-3019 47.1 476 | 6 M341635 53.6 477 | 6 sPb-5030 61 478 | 6 txp145 67.1 479 | 6 sPb-4324 67.1 480 | 6 txp265 67.1 481 | 6 txp274 67.1 482 | 6 sPb-1755 67.1 483 | 6 sPb-7777 67.1 484 | 6 gpsb127 71.2 485 | 6 M188023 71.2 486 | 6 sPb-7290 71.2 487 | 6 M341712 76.5 488 | 6 M340945 76.5 489 | 6 M341047 76.5 490 | 6 M342581 76.5 491 | 6 M342391 76.8 492 | 6 M342764 76.8 493 | 6 gpsb069 77.5 494 | 6 M341192 77.5 495 | 6 sPb-5802 80.5 496 | 6 sPb-7581 80.5 497 | 6 umc44 84.9 498 | 6 sPb-0255 84.9 499 | 6 bnl10.13 88.4 500 | 6 sPb-1721 92.2 501 | 6 M187824 92.2 502 | 6 M189179 94 503 | 6 M343162 94 504 | 6 sPb-3962 94 505 | 6 txp95 95.5 506 | 6 sPb-6526 95.5 507 | 6 M342082 96.1 508 | 6 sPb-5086 98.1 509 | 6 M340415 98.1 510 | 6 sPb-9830 98.1 511 | 6 sPb-5212 98.8 512 | 6 sPb-8954 98.8 513 | 6 sPb-9422 98.8 514 | 6 sPb-0581 99.1 515 | 6 sPb-7627 99.1 516 | 6 sPb-1657 99.1 517 | 6 sPb-8410 99.1 518 | 6 sPb-9667 102.4 519 | 6 M187362 104.5 520 | 6 gpsb169 107.5 521 | 6 SbRPG225 107.8 522 | 6 M340483 107.8 523 | 6 M188251 107.8 524 | 6 M341110 108.9 525 | 6 gpsb019 108.9 526 | 6 M188096 108.9 527 | 6 M341392 109.2 528 | 6 M342492 109.2 529 | 6 M343304 109.2 530 | 6 M342678 109.8 531 | 7 gpsb148 0 532 | 7 msbcir348 0.6 533 | 7 umc85 9.8 534 | 7 M341299 10.5 535 | 7 sPb-1311 13.7 536 | 7 sPb-4878 24.9 537 | 7 sPb-2566 25.2 538 | 7 M342381 27.3 539 | 7 gpsb114 27.3 540 | 7 sPb-5277 29.7 541 | 7 M341472 30.3 542 | 7 sPb-8296 32.1 543 | 7 SbRPG22 34.1 544 | 7 sPb-1244 39 545 | 7 sPb-0508 39.4 546 | 7 sPb-3708 39.4 547 | 7 sPb-7531 39.4 548 | 7 sPb-0571 39.7 549 | 7 sPb-6252 40.5 550 | 7 sPb-2757 40.5 551 | 7 sPb-1414 40.5 552 | 7 sPb-7992 42.4 553 | 7 gap342 45.1 554 | 7 gpsb010 45.8 555 | 7 M343313 51.7 556 | 7 sPb-6863 52.3 557 | 7 sPb-7687 52.3 558 | 7 sPb-3541 52.6 559 | 7 sPb-9206 52.9 560 | 7 sPb-8340 52.9 561 | 7 gpsb031 58.5 562 | 7 msbcir246 62.1 563 | 7 sPb-8251 66.2 564 | 7 sPb-4179 66.2 565 | 7 sPb-9007 66.5 566 | 7 msbcir300 72.5 567 | 7 M340509 72.5 568 | 7 sPb-9351 86.1 569 | 7 txp295 86.4 570 | 7 sPb-6927 92.2 571 | 7 sPb-3258 95.2 572 | 7 sbAGAB02 95.6 573 | 7 sPb-5688 95.9 574 | 7 sPb-9750 96.5 575 | 7 M341979 96.5 576 | 7 bnl5.59 97.1 577 | 7 sPb-6798 98.9 578 | 7 sPb-8608 99.9 579 | 8 sPb-9299 0 580 | 8 txp273 2.1 581 | 8 gpsb041 5.4 582 | 8 gpsb104 11.9 583 | 8 gpsb023 11.9 584 | 8 M188849 14.2 585 | 8 SbRPG931 21.4 586 | 8 sPb-1130 24.3 587 | 8 sPb-5054 24.3 588 | 8 M341589 25.4 589 | 8 sPb-7630 25.4 590 | 8 sPb-5825 28.4 591 | 8 sPb-3247 32.6 592 | 8 sPb-8260 32.6 593 | 8 M341597 34.6 594 | 8 M343025 39.2 595 | 8 M343001 39.2 596 | 8 sPb-9279 39.2 597 | 8 M341867 42.6 598 | 8 sPb-7856 42.6 599 | 8 msbcir240 42.6 600 | 8 sPb-4768 44.7 601 | 8 M340629 50.9 602 | 8 M340408 50.9 603 | 8 M341359 50.9 604 | 8 sPb-2140 50.9 605 | 8 sPb-4200 50.9 606 | 8 sPb-2474 50.9 607 | 8 sPb-8057 50.9 608 | 8 sPb-3195 50.9 609 | 8 SbRPG125 52.9 610 | 8 sPb-9242 56.2 611 | 8 M342657 56.2 612 | 8 gpsb067 58.1 613 | 8 sPb-4934 58.1 614 | 8 sPb-5980 58.8 615 | 8 sPb-2271 58.8 616 | 8 sPb-4432 58.8 617 | 8 sPb-8059 58.8 618 | 8 M187350 58.8 619 | 8 sPb-0435 59.5 620 | 8 sPb-3014 61.5 621 | 8 sPb-9762 65.1 622 | 8 sPb-3236 65.1 623 | 8 sPb-5886 65.1 624 | 8 sPb-0325 65.1 625 | 8 sPb-0441 65.9 626 | 8 sPb-6807 65.9 627 | 8 sPb-3312 66.2 628 | 8 sPb-1265 66.2 629 | 8 sPb-3189 69.2 630 | 8 txp354 69.2 631 | 8 sPb-0258 72.7 632 | 8 gpsb103 80.9 633 | 8 msbcir337a 80.9 634 | 8 sPb-0856 80.9 635 | 8 sPb-7375 85.7 636 | 8 txp18 87.6 637 | 8 txp321 87.6 638 | 8 msbcir304a 87.6 639 | 8 sPb-6935 87.9 640 | 8 sPb-3715 88.9 641 | 8 gpsb002 91 642 | 8 txp105 96.5 643 | 8 gpsb123 96.5 644 | 8 M343181 101 645 | 8 M188408 106.8 646 | 8 M188523 106.8 647 | 8 M342880 107.9 648 | 8 M188830 107.9 649 | 8 M340454 107.9 650 | 8 sPb-8363 107.9 651 | 8 sPb-7220 108.6 652 | 8 sPb-0289 108.6 653 | 8 sPb-2487 108.6 654 | 8 sPb-6960 108.6 655 | 8 sPb-8384 108.6 656 | 8 sPb-6918 108.6 657 | 8 M187818 110 658 | 8 sPb-5401 110 659 | 8 sPb-1051 111.8 660 | 8 sPb-1291 114.3 661 | 9 sPb-4614 0 662 | 9 SbRPG950 2.5 663 | 9 sPb-0445 8.7 664 | 9 sbAGEO3 11.1 665 | 9 umc62 11.1 666 | 9 M187494 11.5 667 | 9 M343363 12.3 668 | 9 M188061 14.6 669 | 9 sPb-7460 15.2 670 | 9 sPb-7686 15.5 671 | 9 gpsb020 15.5 672 | 9 M343231 15.5 673 | 9 sPb-2968 15.5 674 | 9 sPb-1732 15.5 675 | 9 sPb-0852 15.5 676 | 9 sPb-4006 15.5 677 | 9 M189112 19.4 678 | 9 M188652 19.4 679 | 9 M342540 19.4 680 | 9 M342839 22.8 681 | 9 sPb-6089 22.8 682 | 9 gpsb079 22.8 683 | 9 M340730 22.8 684 | 9 M340810 22.8 685 | 9 sPb-1991 24.3 686 | 9 umc38 27.9 687 | 9 sPb-4933 37.9 688 | 9 M341480 42.5 689 | 9 SbRPG743 42.5 690 | 9 M188412 42.5 691 | 9 sPb-0975 42.5 692 | 9 sPb-4416 42.5 693 | 9 M340974 43.5 694 | 9 sPb-8542 47.3 695 | 9 sPb-8777 49 696 | 9 M340536 49.7 697 | 9 sPb-3298 51.5 698 | 9 sPb-6748 51.5 699 | 9 M188560 52.2 700 | 9 sPb-9306 53.5 701 | 9 sPb-8764 56.2 702 | 9 sPb-1324 57.7 703 | 9 sPb-8873 57.7 704 | 9 sPb-4786 57.7 705 | 9 sPb-6852 57.7 706 | 9 sPb-6280 57.7 707 | 9 sPb-6043 57.7 708 | 9 sPb-5055 57.7 709 | 9 M188501 57.7 710 | 9 M342028 57.7 711 | 9 M341817 57.7 712 | 9 sPb-5312 57.7 713 | 9 sPb-3158 57.7 714 | 9 sPb-9272 57.7 715 | 9 SbRPG821 61.8 716 | 9 M342543 67 717 | 9 M341251 67 718 | 9 M342741 67.3 719 | 9 M341021 67.3 720 | 9 M342494 67.3 721 | 9 M187902 67.3 722 | 9 msbcir337b 67.9 723 | 9 msbcir317 67.9 724 | 9 M188207 67.9 725 | 9 sPb-7193 79.9 726 | 9 sPb-0005 79.9 727 | 9 gap42 84.1 728 | 9 M340587 91.3 729 | 9 M342280 91.6 730 | 9 M341115 91.6 731 | 9 umc64 91.6 732 | 9 sPb-6515 92.3 733 | 9 sPb-0303 98.3 734 | 9 sPb-7367 98.3 735 | 9 sPb-7955 98.3 736 | 9 sPb-8368 98.3 737 | 9 sPb-9091 98.3 738 | 9 M340964 98.3 739 | 9 sPb-9410 98.3 740 | 9 sPb-4787 98.3 741 | 9 sPb-9962 98.3 742 | 9 sPb-6506 100.8 743 | 9 M188934 104.1 744 | 9 M341036 104.1 745 | 9 gap85 104.1 746 | 9 sPb-8366 105.3 747 | 10 SbRPG917 0 748 | 10 sPb-5281 0 749 | 10 sPb-8306 0 750 | 10 M340704 1.1 751 | 10 sPb-1945 6.1 752 | 10 WAXY6 10.3 753 | 10 msbcir331 10.9 754 | 10 msbcir324 10.9 755 | 10 M189136 10.9 756 | 10 M342557 22.4 757 | 10 M187940 22.4 758 | 10 sPb-6720 22.4 759 | 10 gpsb027 25 760 | 10 SbRPG921 25 761 | 10 sPb-9517 28.6 762 | 10 sPb-8357 29.4 763 | 10 umc113 30.7 764 | 10 sPb-3828 35.8 765 | 10 M341294 35.8 766 | 10 sPb-1104 35.8 767 | 10 sPb-5917 38.7 768 | 10 sPb-2149 50.3 769 | 10 sPb-0494 52.7 770 | 10 sPb-1962 69.3 771 | 10 M340774 69.3 772 | 10 M342244 69.3 773 | 10 M340871 69.3 774 | 10 M188983 69.3 775 | 10 sPb-9999 71.1 776 | 10 sPb-6331 72.1 777 | 10 M341606 80.7 778 | 10 M341123 81.3 779 | 10 M187142 83.1 780 | 10 txp217 84.7 781 | 10 sPb-4121 84.7 782 | 10 bnl7.24 84.7 783 | 10 msbcir283 85 784 | 10 sPb-6271 85 785 | 10 M340333 90.3 786 | 10 sPb-3696 92.6 787 | 10 sPb-9682 95 788 | 10 sPb-8019 98.2 789 | 10 sPb-2935 98.2 790 | 10 sPb-8232 98.2 791 | 10 SbRPG742 105.8 792 | 10 M342842 114.9 793 | 10 M342954 117.2 794 | 10 M340856 117.2 795 | 10 M340712 117.5 796 | 10 M340495 117.5 797 | 10 gap1 117.5 798 | 10 sPb-0859 124.4 799 | 10 msbcir262 126.2 800 | 10 msbcir227 126.2 801 | 10 sPb-0162 126.2 802 | 10 M343252 138.7 803 | 10 M342053 138.7 804 | 10 M342566 138.7 805 | 10 RZ143 143.8 806 | 10 sPb-4588 147.5 807 | 10 sPb-5814 147.5 808 | 10 gpsb145 148.4 809 | -------------------------------------------------------------------------------- /DATA/SORGHUM/S2: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 sPb-6201 0 3 | 1 sPb-5015 0 4 | 1 txp78 0 5 | 1 sPb-8773 0 6 | 1 ST1380 0 7 | 1 rz630 3.1 8 | 1 sPb-7481 3.1 9 | 1 sPb-3774 3.1 10 | 1 sPb-7096 3.1 11 | 1 sPb-1882 9.7 12 | 1 sPb-1343 18.5 13 | 1 sPb-5605 18.5 14 | 1 Str51 35.3 15 | 1 GE52 63.4 16 | 1 sPb-7398 77.7 17 | 1 ST1700 86.9 18 | 1 sPb-8947 107.7 19 | 1 ST55 107.7 20 | 1 txS361 107.7 21 | 1 SSCIR217 107.7 22 | 1 sPb-1170 107.7 23 | 1 sPb-0090 107.7 24 | 1 RG2931a 107.7 25 | 1 sPb-3386 107.7 26 | 1 sPb-5808 107.7 27 | 1 sPb-6883 107.7 28 | 1 sPb-9514 107.7 29 | 1 ST525 107.7 30 | 1 sPb-6007 107.7 31 | 1 SSCIR107 107.7 32 | 1 SSCIR74 107.7 33 | 1 sPb-0813 107.7 34 | 1 SSCIR74I 107.7 35 | 1 sPb-2058 107.7 36 | 1 sPb-4420 107.7 37 | 1 ST344 109 38 | 1 sPb-0232 116.9 39 | 1 PSB33 116.9 40 | 1 txS1537 122 41 | 1 txS1573 122 42 | 1 sPb-0274 122 43 | 1 sPb-8585 122 44 | 1 EST14 126.8 45 | 1 txp32 126.8 46 | 1 sPb-7567 126.8 47 | 1 cdo454 137.6 48 | 1 ST1847 137.6 49 | 1 ST1277 137.6 50 | 1 EST2 155.3 51 | 1 ST1595 174.5 52 | 1 Str95 174.5 53 | 1 Str80 174.5 54 | 1 Str48 177.2 55 | 1 sPb-5905 177.2 56 | 1 ST1048-2 183.4 57 | 1 ST166 183.4 58 | 1 MT17 186.3 59 | 1 txS1765 189.4 60 | 1 EST40 200.8 61 | 1 EST15 200.8 62 | 1 sPb-0383 200.8 63 | 1 sPb-7154 200.8 64 | 1 sPb-0277 200.8 65 | 1 ST1836-1 201.8 66 | 1 sPb-7611 219.1 67 | 1 txS1153 219.1 68 | 1 sPb-8275 219.1 69 | 1 sPb-8066 219.1 70 | 1 sPb-5527 219.1 71 | 1 sPb-5832 219.1 72 | 1 sPb-5975 219.1 73 | 1 sPb-9314 219.1 74 | 1 sPb-6066 219.1 75 | 1 sPb-1020 219.1 76 | 1 sPb-3577 220.5 77 | 1 Str37 220.5 78 | 1 sPb-1060 220.5 79 | 1 ST1421 223.6 80 | 1 sPb-4435 240.9 81 | 1 FC-6 242.3 82 | 1 ST1200-2 242.3 83 | 1 SSCIR87 242.3 84 | 1 SSCIR73 242.3 85 | 1 GE32 242.3 86 | 2 SSCIR214 0 87 | 2 sPb-0909 3.8 88 | 2 txp80 17.1 89 | 2 sPb-5224 40.5 90 | 2 ST1309 44.6 91 | 2 sPb-1046 45.3 92 | 2 sPb-5880 45.3 93 | 2 txS917b 45.3 94 | 2 sPb-8992 51.4 95 | 2 ST902 52.1 96 | 2 ST1664 52.1 97 | 2 sPb-4970 52.9 98 | 2 txS645c 52.9 99 | 2 FC40b 56.7 100 | 2 PT6 70.1 101 | 2 sPb-1017 75.3 102 | 2 sPb-2639 76.7 103 | 2 sPb-4311 76.7 104 | 2 sPb-2075 76.7 105 | 2 sPb-6700 76.7 106 | 2 sPb-4164 81.6 107 | 2 sPb-5088 81.6 108 | 2 Str29 86.9 109 | 2 PSB416 86.9 110 | 2 SG-3 91.1 111 | 2 Str66 95.3 112 | 2 ST1902 97.9 113 | 2 ST623 101.5 114 | 2 FC28 102.4 115 | 2 sPb-4862 102.4 116 | 2 sPb-3361 102.4 117 | 2 sPb-1925 102.4 118 | 2 ST1300 102.4 119 | 2 sPb-7311 106.2 120 | 2 sPb-0310 106.2 121 | 2 txS1855 106.2 122 | 2 txS443 106.2 123 | 2 txS1925 106.2 124 | 2 ST336 106.2 125 | 2 sPb-0863 106.8 126 | 2 sPb-2360 106.8 127 | 2 sPb-1154 106.8 128 | 2 sPb-0971 106.8 129 | 2 sPb-2080 106.8 130 | 2 txS2042b 106.8 131 | 2 sPb-2424 106.8 132 | 2 sPb-0887 106.8 133 | 2 sPb-5225 106.8 134 | 2 ST109 106.8 135 | 2 sPb-8677 108 136 | 2 sPb-3637 108 137 | 2 MT49 108 138 | 2 sPb-3834 108 139 | 2 sPb-5737 108 140 | 2 SG38 109.3 141 | 2 cdo59 109.3 142 | 2 txS1143 109.8 143 | 2 FC10 116.4 144 | 2 ST359 116.4 145 | 2 sPb-1533 120.1 146 | 2 sPb-0774 120.1 147 | 2 ST1671 123.8 148 | 2 sPb-4005 128.5 149 | 2 sPb-7647 130.7 150 | 2 ST1539 130.7 151 | 2 ST1539b 130.7 152 | 2 txS283 135.5 153 | 2 Str30 135.5 154 | 2 JH-2 173.2 155 | 2 sPb-3086 173.2 156 | 2 MT42 198.7 157 | 2 sPb-1784 211.4 158 | 2 txS1694c 248.1 159 | 2 txS2042 257.8 160 | 2 PSB75 259.3 161 | 2 sPb-3547 259.3 162 | 2 cdo59b 267 163 | 3 SSCIR78 0 164 | 3 sPb-2309 15.1 165 | 3 sPb-3940 23.9 166 | 3 RZ543 34.2 167 | 3 sPb-4226 41.1 168 | 3 ST698 41.1 169 | 3 SSCIR165 41.1 170 | 3 sPb-2562 45.5 171 | 3 ST1537 54.4 172 | 3 PSB517 57.7 173 | 3 txS503 57.7 174 | 3 ST1031 58.2 175 | 3 RZ244 58.2 176 | 3 txS1438 58.2 177 | 3 SG28 58.2 178 | 3 sPb-7675 58.9 179 | 3 SG-51 58.9 180 | 3 sPb-1294 58.9 181 | 3 PSB609 63.5 182 | 3 MT36 64.5 183 | 3 txS1439 64.5 184 | 3 txS536 64.5 185 | 3 ST1176 79.8 186 | 3 txS307 80.8 187 | 3 txS545 81.8 188 | 3 txS545ls 81.8 189 | 3 ST458 85.5 190 | 3 sPb-8349 90.5 191 | 3 S1329 95 192 | 3 RZ444 98.3 193 | 3 ST2032-2 99.9 194 | 3 RZ538 101.5 195 | 3 sPb-7186 101.5 196 | 3 txS301 105.5 197 | 3 sPb-6925 109.1 198 | 3 sPb-3228 109.1 199 | 3 sPb-6649 109.1 200 | 3 txS1075 122.3 201 | 3 txS1075ls 122.8 202 | 3 Str58 135.4 203 | 3 ST329r 174 204 | 3 ST1740 179.7 205 | 3 ST171a 186.5 206 | 4 ST1163-1 0 207 | 4 sPb-4233 1.6 208 | 4 SG30 5.8 209 | 4 GE8 8.5 210 | 4 FC29a 14.9 211 | 4 FC29c 14.9 212 | 4 FC29b 14.9 213 | 4 NEW-7 16.1 214 | 4 sPb-3343 16.1 215 | 4 sPb-6559 16.1 216 | 4 sPb-2009 16.9 217 | 4 sPb-9468 16.9 218 | 4 JH14 22.9 219 | 4 sPb-9580 22.9 220 | 4 sPb-6958 22.9 221 | 4 GE2 24 222 | 4 sPb-7534 35 223 | 4 sPb-4343 35 224 | 4 sPb-8488 35 225 | 4 FC26 35 226 | 4 ST977 38.2 227 | 4 txS1113ls 38.8 228 | 4 txS1113 38.8 229 | 4 sPb-6945 38.8 230 | 4 EST33 44.9 231 | 4 ST517 44.9 232 | 4 sPb-9303 47.2 233 | 4 sPb-8280 47.2 234 | 4 sPb-7794 48.4 235 | 4 sPb-0142 48.4 236 | 4 sPb-6599 48.4 237 | 4 sPb-3981 48.4 238 | 4 cdo1380 60.1 239 | 4 sPb-2689 61.6 240 | 4 txp25 66.1 241 | 6 MT2 0 242 | 6 sPb-2551 0 243 | 6 sPb-9441 0 244 | 6 cdo456 1.5 245 | 6 ST1807 7.2 246 | 6 sPb-8425 30 247 | 6 sPb-3837 30 248 | 6 sPb-2457 30 249 | 6 Str1 33.7 250 | 6 MT8 35.3 251 | 6 SG2 40.1 252 | 6 txS1206 40.1 253 | 6 sPb-9066 42.8 254 | 6 txS1439b 42.8 255 | 6 txS1161 42.8 256 | 6 ST72 42.8 257 | 6 ST1381 42.8 258 | 6 PSB140 54.2 259 | 6 ST982 74.2 260 | 6 sPb-6526 77.8 261 | 6 sPb-5212 85.3 262 | 6 sPb-7428 88.2 263 | 6 EST1 92.5 264 | 6 txS1085 92.5 265 | 6 SG36 95.1 266 | 6 Str65 0 267 | 6 MT22 6.7 268 | 6 MT19 6.7 269 | 6 sPb-3962 16.5 270 | 6 sPb-8954 28.3 271 | 5 sPb-8599 0 272 | 5 txS917 12.1 273 | 5 PSB517b 12.1 274 | 5 sPb-5148 12.1 275 | 5 sPb-5890 16.1 276 | 5 FC4a 21.4 277 | 5 sPb-0381 22.2 278 | 5 sPb-2941 30.6 279 | 5 txp30 30.6 280 | 5 txS722 31.4 281 | 5 sPb-9009 31.4 282 | 5 ST453 31.4 283 | 5 PSB600 31.4 284 | 5 txS2072 31.4 285 | 5 RG5804 31.9 286 | 5 txS387c 49.9 287 | 5 CDSC146 53.6 288 | 5 sPb-6787 54.3 289 | 5 sPb-3766 54.3 290 | 5 sPb-5892 55 291 | 5 sPb-3031 55 292 | 5 sPb-4323 55 293 | 5 sPb-6347 55 294 | 5 txS387 55 295 | 5 FC4b 55 296 | 5 sPb-0205 55 297 | 5 sPb-5209 55.8 298 | 5 sPb-1047 55.8 299 | 5 sPb-4806 55.8 300 | 5 txp15 55.8 301 | 5 sPb-8973 59 302 | 5 txS645b 65 303 | 5 sPb-7203 65 304 | 5 sPb-6323 71.3 305 | 5 sPb-7030 76.9 306 | 5 sPb-1766 76.9 307 | 5 sPb-3817 79.9 308 | 5 sPb-1454 86.6 309 | 5 PSB262 96 310 | 5 cdo520 103.7 311 | 5 RG.S1 117.8 312 | 5 PSB302 118.9 313 | 7 HHU20 0 314 | 7 txp418 2.2 315 | 7 sPb-9931 2.2 316 | 7 txS644 5.2 317 | 7 SG-37 6.7 318 | 7 txS1931 8.2 319 | 7 ST329 10.3 320 | 7 txp159 16.4 321 | 7 FC20 22 322 | 7 txp312 22 323 | 7 sPb-6942 32.6 324 | 7 cdo147 45.8 325 | 7 ST1109 55.9 326 | 7 sPb-9508 58.2 327 | 7 sPb-7944 58.2 328 | 7 sPb-7280 58.2 329 | 7 sPb-6863 58.2 330 | 7 sPb-2774 59.6 331 | 7 sPb-7687 59.6 332 | 7 sPb-3541 59.6 333 | 7 sPb-9206 59.6 334 | 7 Str15 59.6 335 | 7 sPb-8340 59.6 336 | 7 Str44 59.6 337 | 7 RG2331 67.9 338 | 7 sPb-8251 77.9 339 | 7 SSCIR57 77.9 340 | 7 ST1369 93.6 341 | 7 sPb-5397 100.6 342 | 7 sPb-9365 105.6 343 | 7 txp99 105.6 344 | 7 txS1554 106.1 345 | 8 sPb-9299 0 346 | 8 EST27 6 347 | 8 sPb-3341 6 348 | 8 SSCIR55 6 349 | 8 sPb-7126 6 350 | 8 sPb-9818 8 351 | 8 sPb-5390 8 352 | 8 sPb-3536 8 353 | 8 RG8167 8 354 | 8 sPb-8260 14 355 | 8 sPb-3247 14 356 | 8 ST689 14 357 | 8 GE36 16.1 358 | 8 GE59 30.1 359 | 8 sPb-2140 37.9 360 | 8 sPb-3195 42.8 361 | 8 sPb-8057 42.8 362 | 8 sPb-2474 42.8 363 | 8 Str-35 46.3 364 | 8 sPb-9242 46.3 365 | 8 sPb-9841 46.3 366 | 8 ST159 46.3 367 | 8 sPb-4432 46.3 368 | 8 sPb-2271 46.3 369 | 8 sPb-0599 46.3 370 | 8 txS1440 47.3 371 | 8 sPb-3014 47.3 372 | 8 sPb-9602 47.3 373 | 8 Str46 47.3 374 | 8 sPb-0633 47.3 375 | 8 ST1219 47.3 376 | 8 sPb-3312 53.1 377 | 8 sPb-9743 80 378 | 8 sPb-4546 82.3 379 | 8 sPb-0833 82.3 380 | 8 sPb-3954 82.3 381 | 8 sPb-0144 82.3 382 | 8 PS0B527 87.9 383 | 8 PSB0048 87.9 384 | 8 RG1274 96.8 385 | 8 sPb-7823 101.6 386 | 8 sPb-3724 101.6 387 | 8 sPb-0159 101.6 388 | 8 SSCIR211 103.1 389 | 8 RG.T2 105.3 390 | 8 sPb-0289 105.3 391 | 8 sPb-1272 106 392 | 8 sPb-6918 106 393 | 8 sPb-4385 106.6 394 | 8 sPb-7648 106.6 395 | 8 sPb-5401 107.3 396 | 8 sPb-3434 107.3 397 | 8 sPb-7082 107.3 398 | 8 sPb-6960 107.3 399 | 8 sPb-1051 107.3 400 | 8 sPb-1291 108 401 | 9 sPb-6678 0 402 | 9 sPb-4522 0.7 403 | 9 cdo202 3 404 | 9 ST1017 6.7 405 | 9 ST715 6.7 406 | 9 txS1015 11.5 407 | 9 txS307b 11.5 408 | 9 sPb-7460 11.5 409 | 9 sPb-8716 13.7 410 | 9 ST1054 17.4 411 | 9 EST22 17.4 412 | 9 cdo89 22.3 413 | 9 ST329c 27 414 | 9 FC7 32.9 415 | 9 ST1374 38.9 416 | 9 ST171b 38.9 417 | 9 txS1628 38.9 418 | 9 ST1296 38.9 419 | 9 GE5 50.3 420 | 9 SG34 50.3 421 | 9 sPb-6515 68.7 422 | 9 CDS094 69.4 423 | 9 FC34 79.3 424 | 9 NEW-2d 82.4 425 | 9 JH7 82.4 426 | 9 sPb-7367 83.8 427 | 9 sPb-9091 83.8 428 | 9 sPb-8368 83.8 429 | 9 sPb-7955 83.8 430 | 9 sPb-0031 87.2 431 | 9 ST1159 89.4 432 | 10 cdo590c 0 433 | 10 SG33 20.2 434 | 10 cdo590a 20.2 435 | 10 sPb-2041 25 436 | 10 sPb-8306 25 437 | 10 PSB305 25 438 | 10 sPb-6833 25.8 439 | 10 SG19 39.8 440 | 10 ST2072 45.8 441 | 10 sPb-6013 48.2 442 | 10 Str30b 55.4 443 | 10 Str14 55.4 444 | 10 sPb-3432 59.6 445 | 10 sPb-0248 59.6 446 | 10 sPb-5005 60.2 447 | 10 sPb-2193 60.2 448 | 10 txS310 60.9 449 | 10 txS558 62.4 450 | 10 sPb-2222 62.4 451 | 10 sPb-3655 62.4 452 | 10 sPb-2836 62.4 453 | 10 rz144 62.9 454 | 10 sPb-7473 62.9 455 | 10 GE37 66.2 456 | 10 sPb-9999 66.2 457 | 10 sPb-1962 66.2 458 | 10 sPb-4944 66.2 459 | 10 ST403 66.2 460 | 10 GE30 66.2 461 | 10 sPb-6875 66.2 462 | 10 sPb-3958 66.2 463 | 10 sPb-8432 66.2 464 | 10 sPb-4480 66.2 465 | 10 sPb-7643 69 466 | 10 sPb-1722 69 467 | 10 sPb-9215 69 468 | 10 sPb-7284 69 469 | 10 sPb-7948 72.6 470 | 10 sPb-9140 72.6 471 | 10 sPb-9555 74.7 472 | 10 sPb-8019 75.4 473 | 10 ST486-1 76.1 474 | 10 cdo590b 76.1 475 | 10 JH23 76.7 476 | 10 cdo78 82.4 477 | 10 SSCIR63 82.4 478 | 10 sPb-9767 89.7 479 | 10 txS1684 95.1 480 | 10 txS1694 96.1 481 | 10 sPb-0939 97.5 482 | 10 ST1750-2 104.6 483 | 10 FC40 104.6 484 | 10 txS309 108.3 485 | 10 FC48 111.3 486 | 10 ST1845 111.3 487 | 10 PSB619 117.5 488 | 10 ST745 122.3 489 | 10 txS664 125 490 | -------------------------------------------------------------------------------- /DATA/SORGHUM/S4: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 sPb-5611 9.1 3 | 1 sPb-0939 9.1 4 | 1 sPb-4492 9.1 5 | 1 sPb-6160 9.1 6 | 1 sPb-5668 9.1 7 | 1 sPb-8237 9.1 8 | 1 sPb-0602 12 9 | 1 txp350 13.9 10 | 1 txp325 13.9 11 | 1 sPb-5015 15.3 12 | 1 sPb-8773 15.3 13 | 1 sPb-7096 16.6 14 | 1 sPb-7481 17.3 15 | 1 sPb-2704 64 16 | 1 sPb-1694 64 17 | 1 sPb-1119 65.5 18 | 1 sPb-8261 71.8 19 | 1 sPb-6780 83.3 20 | 1 sPb-0422 87.7 21 | 1 sPb-0333 87.7 22 | 1 txp88 87.7 23 | 1 sPb-5808 89.3 24 | 1 sPb-2070 89.3 25 | 1 sPb-9514 89.3 26 | 1 sPb-8794 89.3 27 | 1 sPb-2823 89.3 28 | 1 sPb-6061 89.3 29 | 1 sPb-0090 90.2 30 | 1 sPb-3386 90.2 31 | 1 sPb-6883 90.2 32 | 1 sPb-9050 90.2 33 | 1 sPb-0813 90.2 34 | 1 sPb-2058 90.2 35 | 1 sPb-8947 90.2 36 | 1 sPb-0232 90.2 37 | 1 sPb-2544 90.2 38 | 1 sPb-7229 94.5 39 | 1 sPb-9930 94.5 40 | 1 sPb-0274 94.5 41 | 1 txp37 112.2 42 | 1 sPb-3930 112.2 43 | 1 sPb-0509 112.2 44 | 1 sPb-3801 130.7 45 | 1 sPb-9203 130.7 46 | 1 sPb-3103 130.7 47 | 1 sPb-4638 130.7 48 | 1 sPb-7833 130.7 49 | 1 sPb-7958 137.1 50 | 1 sPb-4029 150.1 51 | 1 sPb-8132 150.1 52 | 1 txp61 160.2 53 | 1 sPb-0277 160.2 54 | 1 sPb-7154 160.2 55 | 1 sPb-0383 160.2 56 | 1 sPb-4593 160.2 57 | 1 sPb-0233 165.2 58 | 1 txp319 171.6 59 | 1 sPb-0725 171.6 60 | 1 sPb-1984 176.5 61 | 1 sPb-8066 177.8 62 | 1 sPb-8275 177.8 63 | 1 sPb-5527 177.8 64 | 1 sPb-5832 177.8 65 | 1 sPb-5975 177.8 66 | 1 sPb-0494 179.5 67 | 1 sPb-7331 179.5 68 | 1 sPb-6066 179.5 69 | 1 sPb-3861 182.4 70 | 1 sPb-4420 182.4 71 | 1 sPb-7850 182.4 72 | 1 txp248 184.6 73 | 1 sPb-2729 184.6 74 | 1 txp316 185.5 75 | 2 sPb-2187 7.6 76 | 2 txp297 18.1 77 | 2 sPb-4195 21.2 78 | 2 txp211 34.2 79 | 2 sPb-4864 38.1 80 | 2 txp50 41.5 81 | 2 txp304 47.5 82 | 2 sPb-1127 48.2 83 | 2 sPb-9687 48.2 84 | 2 sPb-8647 61 85 | 2 sPb-4453 66.2 86 | 2 sPb-6693 72.6 87 | 2 txp13 77.6 88 | 2 sPb-1784 87.5 89 | 2 sPb-1017 88.8 90 | 2 sPb-6700 88.8 91 | 2 sPb-4311 88.8 92 | 2 sPb-2639 88.8 93 | 2 sPb-2075 88.8 94 | 2 sPb-5087 94.3 95 | 2 sPb-1684 95 96 | 2 txp56 99.4 97 | 2 sPb-0073 106.8 98 | 2 sPb-7702 109 99 | 2 Sb6-84 112.5 100 | 2 sPb-4444 116.9 101 | 2 sPb-1925 122.3 102 | 2 sPb-0549 127.5 103 | 2 sPb-0971 132.2 104 | 2 sPb-9511 132.2 105 | 2 sPb-6663 133.4 106 | 2 txp315 133.4 107 | 2 sPb-4054 140 108 | 2 txp207 143.4 109 | 2 sPb-1413 143.4 110 | 2 sPb-7153 145 111 | 2 sPb-5077 161.5 112 | 2 sPb-7647 164.5 113 | 2 sPb-8896 164.5 114 | 2 sPb-7015 171.5 115 | 3 sPb-6690 0 116 | 3 sPb-6982 2 117 | 3 sPb-2461 2 118 | 3 sPb-0892 2.6 119 | 3 sPb-5491 5.7 120 | 3 sPb-4226 8.5 121 | 3 sPb-6122 22.5 122 | 3 sPb-5108 22.5 123 | 3 sPb-1294 25.8 124 | 3 sPb-1901 25.8 125 | 3 sPb-1137 25.8 126 | 3 Sb5-236 25.8 127 | 3 sPb-0357 26.5 128 | 3 sPb-2246 47 129 | 3 sPb-6882 67 130 | 3 sPb-4921 67 131 | 3 sPb-5267 67.7 132 | 3 txp285 68.5 133 | 3 sPb-7186 69.5 134 | 3 sPb-9977 71.8 135 | 3 sPb-4629 71.8 136 | 4 sPb-6640 0 137 | 4 sPb-1932 20.9 138 | 4 sPb-6958 23.5 139 | 4 sPb-9580 23.5 140 | 4 txP22 42.2 141 | 4 sPb-5618 46.3 142 | 4 sPb-2138 46.3 143 | 4 sPb-9635 46.3 144 | 4 sPb-4928 46.3 145 | 4 sPb-2009 46.3 146 | 4 sPb-4039 46.3 147 | 4 sPb-6559 46.3 148 | 4 sPb-8296 46.3 149 | 4 sPb-2699 46.3 150 | 4 sPb-3343 46.3 151 | 4 sPb-7210 46.3 152 | 4 sPb-6231 46.3 153 | 4 sPb-8521 46.3 154 | 4 sPb-9393 46.3 155 | 4 sPb-1811 46.3 156 | 4 sPb-0258 46.3 157 | 4 sPb-0543 46.3 158 | 4 sPb-8138 46.3 159 | 4 sPb-8378 46.3 160 | 4 sPb-1841 46.3 161 | 4 sPb-5276 46.3 162 | 4 sPb-0703 46.8 163 | 4 sPb-1574 46.8 164 | 4 sPb-0279 46.8 165 | 4 sPb-5530 46.8 166 | 4 sPb-5742 46.8 167 | 4 txP343 72.7 168 | 4 sPb-7534 72.7 169 | 4 sPb-1147 73.4 170 | 4 sPb-4825 77.7 171 | 4 sPb-9303 79 172 | 4 sPb-8013 80.2 173 | 4 sPb-6115 80.2 174 | 4 Sb1-10 81.8 175 | 4 sPb-7324 82.6 176 | 4 sPb-9995 83.9 177 | 4 sPb-9713 87.7 178 | 4 sPb-3838 90.4 179 | 4 sPb-4734 90.4 180 | 4 sPb-4473 93.6 181 | 4 sPb-4198 93.6 182 | 4 sPb-9304 93.6 183 | 4 sPb-8337 93.6 184 | 4 sPb-4070 99.5 185 | 4 txP177 105.2 186 | 4 txP41 105.2 187 | 4 txP51 105.2 188 | 4 txP60 108 189 | 4 tXP265 126 190 | 4 sPb-7434 127.3 191 | 4 sPb-5550 133.9 192 | 5 sPb-0906 0 193 | 5 txp65 11.7 194 | 5 sPb-7047 11.7 195 | 5 sPb-5256 13.3 196 | 5 sPb-7308 19.9 197 | 5 sPb-2941 31.9 198 | 5 sPb-6627 39.7 199 | 5 sPb-9815 39.7 200 | 5 sPb-2539 39.7 201 | 5 sPb-4131 39.7 202 | 5 sPb-7638 39.7 203 | 5 sPb-3290 39.7 204 | 5 sPb-1227 39.7 205 | 5 sPb-3977 39.7 206 | 5 sPb-8881 45.3 207 | 5 sPb-9544 47.2 208 | 5 sPb-5805 50 209 | 5 sPb-8316 50 210 | 5 sPb-9009 50.7 211 | 5 sPb-0932 57.1 212 | 5 txp15 69.4 213 | 5 sPb-0738 70.2 214 | 5 sPb-3031 71.5 215 | 5 sPb-6508 71.5 216 | 5 sPb-5194 71.5 217 | 5 sPb-3766 72.2 218 | 5 sPb-0368 73.3 219 | 5 sPb-1501 77.6 220 | 5 sPb-9347 77.6 221 | 5 sPb-8677 79.7 222 | 5 sPb-6636 79.7 223 | 5 sPb-6347 80.3 224 | 5 sPb-0205 80.3 225 | 5 sPb-1104 80.3 226 | 5 sPb-4806 81 227 | 5 sPb-5209 81 228 | 5 sPb-6604 82.9 229 | 5 sPb-8973 82.9 230 | 5 sPb-7203 86.6 231 | 5 sPb-6323 86.6 232 | 5 sPb-6855 86.6 233 | 5 sPb-3817 93.1 234 | 5 sPb-1152 98.5 235 | 5 sPb-1454 99.2 236 | 5 SbKAFGK1_I 108.8 237 | 5 SBKAFGK1 113.2 238 | 5 sPb-2874 119.4 239 | 6 sPb-4183 0 240 | 6 sPb-4937 2.9 241 | 6 sPb-5182 2.9 242 | 6 sPb-2551 9 243 | 6 sPb-3539 11.8 244 | 6 txp6 16.9 245 | 6 sPb-8422 23.5 246 | 6 sPb-5564 27.5 247 | 6 sPb-1395 27.5 248 | 6 sPb-7169 27.5 249 | 6 sPb-8198 27.5 250 | 6 sPb-8362 28.1 251 | 6 sPb-9146 38.5 252 | 6 sPb-1543 57.7 253 | 6 txp145 60.2 254 | 6 txp265-1 64.4 255 | 6 txp274 67.4 256 | 6 CC 91.5 257 | 6 sPb-5374 115.3 258 | 6 sPb-3962 125.6 259 | 6 sPb-0017 131.9 260 | 6 sPb-5603 131.9 261 | 6 sPb-1486 131.9 262 | 6 sPb-4732 131.9 263 | 6 txp17 131.9 264 | 6 txp95 131.9 265 | 6 txp57 136.4 266 | 6 sPb-5212 136.4 267 | 6 sPb-6076 136.4 268 | 6 sPb-9667 141.4 269 | 6 sPb-7428 144.3 270 | 7 sPb-9595 2.7 271 | 7 sPb-7064 2.7 272 | 7 sPb-4740 2.7 273 | 7 sPb-3995 2.7 274 | 7 sPb-2034 2.7 275 | 7 sPb-9223 4.7 276 | 7 sPb-9931 6 277 | 7 sPb-9513 18.6 278 | 7 sPb-3361 18.6 279 | 7 sPb-6518 20.6 280 | 7 txp159 28 281 | 7 sPb-8216 31.3 282 | 7 sPb-2566 43.5 283 | 7 sPb-6942 47 284 | 7 sPb-2774 47 285 | 7 txp227 49.5 286 | 7 sPb-2757 49.5 287 | 7 sPb-1414 53.4 288 | 7 sPb-8019 53.4 289 | 7 sPb-8258 53.4 290 | 7 sPb-5594 73.9 291 | 7 sPb-7086 91.9 292 | 7 sPb-3691 95.3 293 | 7 txp295 95.3 294 | 7 sPb-4306 107.8 295 | 7 sPb-1014 118.4 296 | 7 txp168 119.1 297 | 7 sPb-7549 120.5 298 | 8 txp273 0 299 | 8 sPb-6589 0 300 | 8 sPb-2736 6.3 301 | 8 sPb-3464 6.3 302 | 8 sPb-5390 23.9 303 | 8 sPb-7126 23.9 304 | 8 sPb-9818 23.9 305 | 8 sPb-5825 23.9 306 | 8 sPb-3396 28.4 307 | 8 sPb-9372 59.6 308 | 8 sPb-5005 59.6 309 | 8 sPb-2140 63 310 | 8 sPb-4934 63 311 | 8 sPb-2641 65 312 | 8 sPb-1058 73.2 313 | 8 sPb-2474 73.2 314 | 8 sPb-3195 73.2 315 | 8 sPb-2568 74.4 316 | 8 txp210 74.4 317 | 8 sPb-0599 79.8 318 | 8 sPb-9841 79.8 319 | 8 sPb-9762 82.5 320 | 8 sPb-9700 86.4 321 | 8 sPb-9468 87 322 | 8 sPb-7889 88.9 323 | 8 sPb-1881 99.2 324 | 8 sPb-1595 99.2 325 | 8 txp18 99.2 326 | 8 sPb-1850 99.2 327 | 8 sPb-7375 101.3 328 | 8 sPb-0031 111.3 329 | 8 sPb-2228 111.3 330 | 8 sPb-9743 111.3 331 | 8 sPb-3014 111.3 332 | 8 sPb-3954 111.3 333 | 8 sPb-0833 111.3 334 | 8 txp105 115.1 335 | 8 sPb-6918 126.2 336 | 8 sPb-5401 129 337 | 8 sPb-1272 133.1 338 | 8 sPb-8993 133.1 339 | 8 sPb-9584 133.1 340 | 8 sPb-6960 133.9 341 | 8 sPb-5250 133.9 342 | 8 sPb-2846 133.9 343 | 8 sPb-7648 134.5 344 | 8 sPb-4385 134.5 345 | 8 sPb-3434 135.9 346 | 8 sPb-0087 140.2 347 | 8 sPb-0144 178.2 348 | 9.1 sPb-5473 0 349 | 9.1 sPb-3274 1.4 350 | 9.1 sPb-1997 2.1 351 | 9.1 sPb-9449 3.5 352 | 9.1 sPb-2770 4.2 353 | 9.2 sPb-0005 0 354 | 9.2 txp258 4.9 355 | 9.2 sPb-9989 50.8 356 | 9.2 sPb-9688 56.6 357 | 9.2 sPb-8873 59.3 358 | 9.2 sPb-4786 59.3 359 | 9.2 sPb-1324 59.9 360 | 9.2 sPb-6852 59.9 361 | 9.2 sPb-9272 61.3 362 | 9.2 sPb-3158 61.3 363 | 9.2 sPb-5312 61.3 364 | 9.2 sPb-5142 76 365 | 9.2 sPb-3971 100 366 | 9.2 sPb-9107 100 367 | 9.2 Sb4-32 101.9 368 | 9.2 sPb-4853 105.5 369 | 9.2 sPb-9227 105.5 370 | 9.2 sPb-7955 153.6 371 | 9.2 sPb-9091 154.3 372 | 9.2 sPb-8368 154.3 373 | 9.2 sPb-7367 154.3 374 | 10 sPb-3912 0 375 | 10 sPb-8306 6.8 376 | 10 sPb-2041 6.8 377 | 10 sPb-5079 6.8 378 | 10 RL 6.8 379 | 10 sPb-4129 8.7 380 | 10 sPb-8858 58.1 381 | 10 sPb-3432 63.4 382 | 10 sPb-0248 63.4 383 | 10 sPb-3287 69.9 384 | 10 sPb-8150 69.9 385 | 10 sPb-0817 69.9 386 | 10 sPb-2149 69.9 387 | 10 sPb-5391 69.9 388 | 10 sPb-8497 69.9 389 | 10 sPb-0562 69.9 390 | 10 sPb-2836 71.1 391 | 10 sPb-3549 71.1 392 | 10 sPb-9999 71.1 393 | 10 sPb-7643 80.7 394 | 10 sPb-3958 80.7 395 | 10 sPb-9215 80.7 396 | 10 sPb-5512 80.7 397 | 10 sPb-6875 80.7 398 | 10 sPb-4944 81.4 399 | 10 sPb-1244 82.6 400 | 10 SvPEPCAA 87 401 | 10 sPb-6889 87 402 | 10 sPb-5841 90.3 403 | 10 sPb-6288 93.7 404 | 10 sPb-4480 93.7 405 | 10 sPb-2318 93.7 406 | 10 sPb-3003 126.5 407 | 10 sPb-1701 126.5 408 | 10 sPb-1660 126.5 409 | 10 txp141 140.7 410 | 10 Sb6-325 140.7 411 | 10 sPb-5678 149.2 412 | -------------------------------------------------------------------------------- /DATA/SORGHUM/S5: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 sPb-7015 0 3 | 1 sPb-2465 1.9 4 | 1 sPb-8208 2.6 5 | 1 sPb-2058 32.6 6 | 1 sPb-0232 47.6 7 | 1 sPb-0274 53.6 8 | 1 sPb-5358 67.6 9 | 1 txp58 100 10 | 1 sPb-9203 120 11 | 1 sPb-2683 120 12 | 1 sPb-4638 126 13 | 1 txp279 130 14 | 1 txp284 141.2 15 | 1 sPb-0277 156.2 16 | 1 sPb-4593 156.2 17 | 1 sPb-0746 156.2 18 | 1 sPb-1984 175 19 | 1 sPb-7611 190.4 20 | 1 sPb-3577 207.7 21 | 1 sPb-1060 207.7 22 | 2 sPb-7984 0 23 | 2 sPb-8897 2.4 24 | 2 sPb-1631 5.3 25 | 2 txp4 22.6 26 | 2 txp3 25.7 27 | 2 sPb-3361 50 28 | 2 sPb-5737 51.3 29 | 2 sPb-3834 51.3 30 | 2 sPb-4054 66 31 | 3 sPb-1669 0 32 | 3 sPb-2055 0 33 | 3 sPb-6690 30 34 | 3 sPb-6857 30 35 | 3 sPb-1137 46.2 36 | 3 sPb-1362 46.2 37 | 3 sPb-5108 46.2 38 | 3 sPb-6122 47.5 39 | 3 sPb-8787 66.4 40 | 3 sPb-8349 115 41 | 3 txp285 128 42 | 4 sPb-6465 0 43 | 4 sPb-2521 2 44 | 4 sPb-4074 5 45 | 4 sPb-8446 5 46 | 4 sPb-2739 5 47 | 4 sPb-7493 5 48 | 4 sPb-4718 5 49 | 4 sPb-1584 8.8 50 | 4 sPb-6741 40 51 | 4 sPb-4506 40 52 | 4 sPb-2138 40 53 | 4 sPb-8296 40 54 | 4 sPb-6559 40 55 | 4 sPb-1811 40 56 | 4 sPb-1798 40 57 | 4 sPb-9359 40 58 | 4 sPb-7310 40 59 | 4 sPb-7274 40 60 | 4 sPb-5742 40 61 | 4 sPb-1841 40 62 | 4 sPb-9987 40 63 | 4 sPb-0867 40 64 | 4 sPb-7210 40 65 | 4 sPb-9033 40 66 | 4 sPb-3903 40 67 | 4 sPb-1295 40 68 | 4 sPb-8470 40 69 | 4 sPb-2187 47.3 70 | 4 sPb-2842 67.4 71 | 4 sPb-5220 79.7 72 | 4 txp343 86.5 73 | 4 SB1-10 98.1 74 | 4 sPb-9303 106.9 75 | 4 sPb-6115 106.9 76 | 5 sPb-5119 0 77 | 5 sPb-9544 0 78 | 5 sPb-5408 0.6 79 | 5 sPb-9009 2.7 80 | 5 txp303 9.7 81 | 5 sPb-5892 31.9 82 | 5 sPb-9642 35.7 83 | 5 sPb-8149 38.2 84 | 5 sPb-6323 38.2 85 | 5 sPb-7203 45.6 86 | 5 sPb-1454 65 87 | 5 sPb-6287 69.4 88 | 5 sPb-1396 94.4 89 | 5 sPb-2874 95.1 90 | 5 sPb-0873 95.1 91 | 5 sPb-7892 118.2 92 | 6 sPb-5603 0 93 | 6 sPb-0017 0 94 | 6 sPb-4732 0 95 | 6 sPb-1486 0 96 | 6 sPb-5101 5.6 97 | 7 sPb-8316 0 98 | 7 sPb-7064 0 99 | 7 sPb-4740 0 100 | 7 sPb-9309 0 101 | 7 sPb-2034 1.4 102 | 7 sPb-1414 30 103 | 7 sPb-2757 30 104 | 7 sPb-1244 30 105 | 7 sPb-0571 30 106 | 7 sPb-2774 34 107 | 7 sPb-8673 39.5 108 | 7 txp312 59.4 109 | 7 sPb-6577 79.5 110 | 7 sPb-9007 104.5 111 | 7 sPb-8251 104.5 112 | 7 sPb-4179 104.5 113 | 7 sPb-2491 127.5 114 | 8 sPb-6248 0 115 | 8 sPb-0703 13.2 116 | 8 sPb-7126 13.2 117 | 8 sPb-9818 22 118 | 8 sPb-7843 22 119 | 8 sPb-3396 23.2 120 | 8 sPb-9242 55 121 | 8 sPb-4432 57 122 | 8 sPb-0258 76.3 123 | 8 sPb-9700 83.4 124 | 8 sPb-1661 88.9 125 | 8 sPb-7889 89.5 126 | 8 sPb-7312 103 127 | 8 sPb-1881 110.2 128 | 8 sPb-2228 122.4 129 | 8 sPb-9743 122.4 130 | 8 sPb-0144 122.4 131 | 8 sPb-4546 122.4 132 | 8 sPb-0833 122.4 133 | 8 sPb-3954 123 134 | 8 sPb-7823 141.1 135 | 8 sPb-6918 156.9 136 | 8 sPb-1825 156.9 137 | 8 sPb-5401 161.9 138 | 8 sPb-1291 161.9 139 | 8 sPb-1051 161.9 140 | 8 sPb-1272 164 141 | 8 sPb-7082 164 142 | 8 sPb-5250 164.8 143 | 8 sPb-7648 165.5 144 | 8 sPb-2290 167.5 145 | 8 sPb-5420 168 146 | 8 sPb-4385 169 147 | 8 sPb-3434 173.9 148 | 9 sPb-7855 0 149 | 9 sPb-1991 7.6 150 | 9 sPb-8812 12 151 | 9 sPb-9227 12 152 | 9 sPb-3253 40 153 | 9 sPb-9538 40 154 | 9 sPb-7105 50.3 155 | 9 sPb-7905 69.7 156 | 9 sPb-1239 69.7 157 | 9 sPb-0771 69.7 158 | 9 sPb-8338 69.7 159 | 9 sPb-6667 84 160 | 9 txp230 84 161 | 9 txp67 90.2 162 | 9 txp258 97.1 163 | 9 sPb-4156 97.1 164 | 9 sPb-6515 117.3 165 | 10 sPb-8306 0 166 | 10 sPb-2735 0.7 167 | 10 sPb-2193 40.3 168 | 10 sPb-3432 40.3 169 | 10 sPb-0248 40.3 170 | 10 sPb-5005 41 171 | 10 sPb-8357 45 172 | 10 sPb-9592 47.75 173 | 10 sPb-0817 50.8 174 | 10 sPb-8150 50.8 175 | 10 sPb-6292 50.8 176 | 10 sPb-5917 58.2 177 | 10 sPb-7473 61.85 178 | 10 sPb-1962 69.75 179 | 10 sPb-5841 77.75 180 | 10 sPb-9064 77.75 181 | 10 sPb-8201 77.75 182 | 10 sPb-6889 84.75 183 | 10 sPb-9215 84.75 184 | 10 sPb-1722 84.75 185 | 10 sPb-3958 84.75 186 | 10 sPb-8432 84.75 187 | 10 sPb-7643 84.75 188 | 10 sPb-4944 84.75 189 | 10 sPb-6875 84.75 190 | 10 sPb-4480 86.75 191 | -------------------------------------------------------------------------------- /DATA/SORGHUM/S6: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 sPb-0145 0 3 | 1 sPb-2823 8.8 4 | 1 sPb-6061 10.1 5 | 1 sPb-0090 14.1 6 | 1 sPb-3386 14.1 7 | 1 sPb-0232 20.9 8 | 2 sPb-3798 0 9 | 2 sPb-8616 19.7 10 | 2 sPb-7695 23 11 | 2 sPb-4970 26 12 | 2 sPb-6434 29 13 | 2 sPb-1784 42.7 14 | 2 sPb-1017 46 15 | 2 sPb-2075 47 16 | 2 sPb-2639 47 17 | 2 sPb-4311 47 18 | 2 sPb-6700 47.5 19 | 2 sPb-3361 68.6 20 | 2 sPb-3834 73.3 21 | 2 sPb-5737 76 22 | 2 sPb-8677 88.1 23 | 3 sPb-3853 0 24 | 3 sPb-9388 2.4 25 | 3 sPb-6857 9.7 26 | 3 sPb-3940 13.8 27 | 3 sPb-2562 36.2 28 | 3 sPb-8536 75 29 | 3 sPb-6882 75 30 | 3 sPb-4921 87.3 31 | 3 sPb-7186 97.7 32 | 3 sPb-4629 103.5 33 | 3 sPb-9894 113.1 34 | 3 sPb-8802 145 35 | 4 sPb-8138 0 36 | 4 sPb-4661 4.6 37 | 4 sPb-4734 36.9 38 | 4 sPb-9304 42 39 | 5 sPb-0906 0 40 | 5 sPb-5890 1.4 41 | 5 sPb-9312 30 42 | 5 sPb-7893 30 43 | 5 sPb-7030 36.6 44 | 5 sPb-9490 58.4 45 | 6 sPb-2551 0 46 | 6 sPb-3837 21.3 47 | 6 sPb-2457 21.3 48 | 6 sPb-8425 21.3 49 | 6 sPb-5030 27.8 50 | 6 sPb-9066 40.7 51 | 6 sPb-8436 43.7 52 | 6 sPb-1543 53.8 53 | 6 sPb-5230 83.8 54 | 6 sPb-0255 90.8 55 | 6 sPb-5403 90.8 56 | 6 sPb-7960 90.8 57 | 6 sPb-5765 97.5 58 | 7 sPb-7105 0 59 | 7 sPb-9931 19.1 60 | 7 sPb-8980 19.1 61 | 7 sPb-6825 50 62 | 7 sPb-1982 54.9 63 | 7 sPb-7381 62.2 64 | 7 sPb-8673 87.2 65 | 7 sPb-2630 102.9 66 | 7 sPb-9508 105.5 67 | 7 sPb-7944 107.4 68 | 7 sPb-7280 116.9 69 | 7 sPb-7086 141.9 70 | 7 sPb-5397 146.8 71 | 7 sPb-9365 146.8 72 | 8 sPb-6589 0 73 | 8 sPb-1503 2.5 74 | 8 sPb-9818 29 75 | 8 sPb-7126 30.3 76 | 8 sPb-5054 32.9 77 | 8 sPb-9372 65 78 | 8 sPb-0235 65 79 | 8 sPb-0645 65.6 80 | 8 sPb-0380 85 81 | 8 sPb-1888 105.3 82 | 8 sPb-1272 130 83 | 8 sPb-5250 130 84 | 8 sPb-2846 133.2 85 | 8 sPb-9584 134.5 86 | 8 sPb-4385 141 87 | 8 sPb-2487 147 88 | 8 sPb-1323 159.5 89 | 9.1 sPb-3274 0 90 | 9.1 sPb-1997 0.7 91 | 9.1 sPb-9449 0.7 92 | 9.1 sPb-8090 8.5 93 | 9.1 sPb-8590 9 94 | 9.1 sPb-4011 17 95 | 9.1 sPb-1895 17.5 96 | 9.2 sPb-7460 0 97 | 9.2 sPb-8716 3.3 98 | 9.2 sPb-8812 11.8 99 | 9.2 sPb-9227 11.8 100 | 9.2 sPb-4156 40 101 | 9.2 sPb-0005 46.85 102 | 10 sPb-2222 0 103 | 10 sPb-7473 14.35 104 | 10 sPb-9381 16.2 105 | 10 sPb-9999 19.9 106 | 10 sPb-9215 27.7 107 | 10 sPb-3958 27.7 108 | 10 sPb-1722 27.7 109 | 10 sPb-4480 27.7 110 | 10 sPb-8432 27.7 111 | 10 sPb-6875 27.7 112 | 10 sPb-7643 27.7 113 | 10 sPb-9826 29.95 114 | 10 sPb-5834 44.3 115 | 10 sPb-6288 59.6 116 | 10 sPb-5841 61.5 117 | 10 sPb-6889 62.8 118 | 10 sPb-8201 62.8 119 | -------------------------------------------------------------------------------- /DATA/SORGHUM/TAMU: -------------------------------------------------------------------------------- 1 | LG Marker cM 2 | 1 isu62 0 3 | 1 cdo457 6.5 4 | 1 umc84 6.5 5 | 1 bnl6.25 6.5 6 | 1 isu70 9.6 7 | 1 sPb-1524 9.6 8 | 1 sPb-6144 9.6 9 | 1 sPb-8773 9.6 10 | 1 sPb-5015 9.6 11 | 1 umc161 13 12 | 1 sPb-7161 13.8 13 | 1 gap42 13.8 14 | 1 gap256 13.8 15 | 1 umc147 14.5 16 | 1 sPb-2583 14.5 17 | 1 phyC 14.5 18 | 1 txp208 14.5 19 | 1 txs1082 15.3 20 | 1 txp325 17.4 21 | 1 txp350 17.4 22 | 1 sPb-6201 17.4 23 | 1 cup06 19.6 24 | 1 isu111 20.1 25 | 1 txs547 21.1 26 | 1 umc90 22.4 27 | 1 phyA 22.4 28 | 1 bcd808 23.6 29 | 1 txp482 23.6 30 | 1 cdo121 23.6 31 | 1 txp302 25.2 32 | 1 umc128 39.9 33 | 1 umc166 45.8 34 | 1 sPb-2070 45.8 35 | 1 umc83 46.7 36 | 1 sPb-2823 55.1 37 | 1 sPb-8794 55.1 38 | 1 umc115 55.1 39 | 1 sPb-8612 55.8 40 | 1 sPb-4227 57.2 41 | 1 umc167 59.5 42 | 1 CSU653 59.5 43 | 1 txp357 59.5 44 | 1 txp43 65.8 45 | 1 txp88 65.8 46 | 1 isu73 69.5 47 | 1 txp149 69.5 48 | 1 txs1060 73.6 49 | 1 sPb-0274 73.6 50 | 1 txs490 74.4 51 | 1 sPb-5358 76.4 52 | 1 txp32 76.4 53 | 1 cdo20 77.8 54 | 1 isu165 84.9 55 | 1 sPb-9560 88.8 56 | 1 txp37 92.4 57 | 1 isu164 97.9 58 | 1 sPb-8883 97.9 59 | 1 sPb-1935 97.9 60 | 1 txp335 97.9 61 | 1 isu44 98.3 62 | 1 rz251 98.3 63 | 1 sPb-7031 98.3 64 | 1 txs1129 100.5 65 | 1 txs1225 105.5 66 | 1 sPb-3844 108.9 67 | 1 sPb-5223 108.9 68 | 1 umc95 108.9 69 | 1 gap57 108.9 70 | 1 txp58 109.7 71 | 1 sPb-5905 112.3 72 | 1 cdo938 112.3 73 | 1 isu51 115.1 74 | 1 sPb-2147 115.1 75 | 1 txp75 115.1 76 | 1 txp279 115.1 77 | 1 phyB 116 78 | 1 OP-K20C 116 79 | 1 txp229 116 80 | 1 cup60 123.4 81 | 1 txs1765 127.7 82 | 1 gap36 127.7 83 | 1 isu133 127.7 84 | 1 OP-K20A 130 85 | 1 sPb-9156 132.6 86 | 1 sPb-7422 132.6 87 | 1 cup30 135.3 88 | 1 cdo1387 137.3 89 | 1 txp522 137.3 90 | 1 txp433 137.7 91 | 1 txp284 137.7 92 | 1 txp432 138.5 93 | 1 txp61 138.5 94 | 1 isu49 138.5 95 | 1 sPb-0383 139.7 96 | 1 txs300 139.7 97 | 1 bnl5.09 141 98 | 1 bnl14.28 143.2 99 | 1 txs1625 156.8 100 | 1 txp319 158 101 | 1 txp515 158.8 102 | 1 txp519 158.8 103 | 1 rz329 159.6 104 | 1 txp340 159.6 105 | 1 umc157 163.2 106 | 1 sPb-4582 166.8 107 | 1 sPb-5832 166.8 108 | 1 sPb-8066 166.8 109 | 1 sPb-8275 166.8 110 | 1 sPb-5527 166.8 111 | 1 txs1153 166.8 112 | 1 sPb-1020 168.1 113 | 1 sPb-1060 172.2 114 | 1 sPb-7611 172.2 115 | 1 sPb-3577 173.6 116 | 1 rz273 175.7 117 | 1 txp248 179.6 118 | 1 sPb-9065 179.6 119 | 1 sPb-4435 181.7 120 | 1 txp316 181-7 121 | 1 sPb-1050 181-7 122 | 1 sPb-8743 181-7 123 | 1 AW747649 181-7 124 | 1 txp46 183.2 125 | 1 umc164 191.8 126 | 2 txp197 0 127 | 2 txp96 0 128 | 2 txs1267 0 129 | 2 txp63 4.3 130 | 2 sPb-1209 18 131 | 2 txp25 20.5 132 | 2 txp84 20.5 133 | 2 txp211 20.5 134 | 2 txp50 20.5 135 | 2 isu67 20.5 136 | 2 sPb-6689 20.5 137 | 2 txp297 24.1 138 | 2 txp304 36.8 139 | 2 txs1891 50.2 140 | 2 isu71 58.3 141 | 2 sPb-0090 58.3 142 | 2 sPb-3386 58.3 143 | 2 txs1164 58.3 144 | 2 sPb-5643 58.3 145 | 2 sPb-1940 58.3 146 | 2 txs1381 59.2 147 | 2 sPb-9708 59.2 148 | 2 sPb-5224 63.3 149 | 2 sPb-6360 63.3 150 | 2 sPb-7142 63.3 151 | 2 sPb-0364 63.3 152 | 2 txp4 64.8 153 | 2 sPb-8616 67 154 | 2 isu151 69.2 155 | 2 txp201 73.9 156 | 2 cup74 73.9 157 | 2 txp55 73.9 158 | 2 txp3 73.9 159 | 2 txp72 73.9 160 | 2 sPb-4970 73.9 161 | 2 sPb-6434 73.9 162 | 2 rz413 75.6 163 | 2 sPb-6724 77.8 164 | 2 txs1639 77.8 165 | 2 txp19 80 166 | 2 txp283 80 167 | 2 txp13 82 168 | 2 isu94 85.1 169 | 2 txp298 92.9 170 | 2 SbAGAB03 97.7 171 | 2 umc139 102.2 172 | 2 sPb-5087 108.7 173 | 2 sPb-2484 118.6 174 | 2 txp445 121.7 175 | 2 cup29 121.7 176 | 2 CB926473 123.7 177 | 2 txp430 123.7 178 | 2 sPb-2129 123.7 179 | 2 txs1111 123.7 180 | 2 isu86 123.7 181 | 2 umc136 123.7 182 | 2 umc5 124.2 183 | 2 txp1 126 184 | 2 txp56 129.8 185 | 2 sPb-3917 133.1 186 | 2 umc116 133.1 187 | 2 sPb-2568 137.4 188 | 2 txp286 137.4 189 | 2 gap84 140.7 190 | 2 sPb-4862 140.7 191 | 2 sPb-3361 140.7 192 | 2 isu117 143.7 193 | 2 isu43 145.4 194 | 2 sPb-1925 145.4 195 | 2 txp179 145.4 196 | 2 sPb-4685 145.4 197 | 2 txs284 146.7 198 | 2 isu66 151.7 199 | 2 txs1527 151.7 200 | 2 sPb-0971 151.7 201 | 2 sPb-0863 151.7 202 | 2 cdo1508 151.7 203 | 2 txp428 153.2 204 | 2 sPb-6009 155.1 205 | 2 txp315 155.1 206 | 2 txs2042 155.1 207 | 2 cdo385 155.1 208 | 2 txs360 155.8 209 | 2 sPb-5478 155.8 210 | 2 txp431 156.6 211 | 2 txp429 158.7 212 | 2 txs1925 160.5 213 | 2 umc4 162.4 214 | 2 cdo59 165.3 215 | 2 txp100 165.3 216 | 2 sPb-3547 166.8 217 | 2 umc149 166.8 218 | 2 txp7 166.8 219 | 2 txp207 166.8 220 | 2 cup26 166.8 221 | 2 sPb-1413 168.2 222 | 2 txs1845 168.2 223 | 2 txp296 168.2 224 | 2 umc125 175.3 225 | 2 sPb-0774 175.3 226 | 2 sPb-8602 175.3 227 | 2 sPb-2350 175.9 228 | 2 umc122 175.9 229 | 2 sPb-5286 176.6 230 | 2 sPb-3683 176.6 231 | 2 bnl8.37 176.6 232 | 2 rz395 179.3 233 | 2 sPb-1533 182.3 234 | 2 txs290 182.3 235 | 2 isu113 182.3 236 | 2 txs283 183 237 | 2 cup40 190 238 | 2 txp8 193.8 239 | 2 cup69 198.5 240 | 2 txs1610 200.6 241 | 2 umc168 202.5 242 | 2 sPb-4005 208.1 243 | 2 bnl16.06 208.1 244 | 2 sPb-8896 209.6 245 | 2 sPb-9190 215.4 246 | 2 sPb-2833 219.6 247 | 2 sPb-3109 229.6 248 | 3 txs333 0 249 | 3 txp496 0 250 | 3 txp456 0 251 | 3 txp494 4 252 | 3 sPb-2249 4 253 | 3 sPb-7795 4 254 | 3 umc121 4 255 | 3 txp457 4.4 256 | 3 txp266 6.1 257 | 3 txs31 6.1 258 | 3 txs1178 6.1 259 | 3 txp228 6.1 260 | 3 txp518 6.5 261 | 3 txp454 9.3 262 | 3 txp492 11.1 263 | 3 txp491 17.3 264 | 3 txs1092 24.6 265 | 3 sPb-0319 28.9 266 | 3 txp489 28.9 267 | 3 txp451 28.9 268 | 3 txp215 28.9 269 | 3 txp452 30.2 270 | 3 txp423 36.5 271 | 3 txp488 36.5 272 | 3 txs1053 38.4 273 | 3 BH245389 40.1 274 | 3 txp485 40.1 275 | 3 sPb-6690 42 276 | 3 sPb-4639 45.4 277 | 3 txs578 45.4 278 | 3 txp500 45.4 279 | 3 isu148 48.9 280 | 3 txs503 51.5 281 | 3 BH246082 51.5 282 | 3 txs1438 55.9 283 | 3 txp461 55.9 284 | 3 txs1162 55.9 285 | 3 sPb-1137 55.9 286 | 3 sPb-6122 55.9 287 | 3 sPb-5108 55.9 288 | 3 sPb-1362 55.9 289 | 3 umc10 56.6 290 | 3 sPb-5946 56.6 291 | 3 rz244 57.5 292 | 3 cdo920 59.8 293 | 3 txp33 59.8 294 | 3 gap236 62.8 295 | 3 isu114 65.1 296 | 3 txp205 65.1 297 | 3 txp31 71.1 298 | 3 sPb-1700 71.1 299 | 3 txp336 71.1 300 | 3 txp183 75.2 301 | 3 txp444 80.2 302 | 3 txs584 85.4 303 | 3 txp503 87.5 304 | 3 umc93 87.5 305 | 3 umc63 87.5 306 | 3 txp120 87.5 307 | 3 txp435 89.2 308 | 3 txp2 91.7 309 | 3 cdo1160 98.9 310 | 3 txp436 98.9 311 | 3 txp231 98.9 312 | 3 txp59 103.2 313 | 3 bcd828 110.2 314 | 3 sPb-9524 110.2 315 | 3 txs1175 110.2 316 | 3 cdo470 110.2 317 | 3 txp218 110.2 318 | 3 umc16 110.2 319 | 3 umc17 110.2 320 | 3 isu121 111.5 321 | 3 txp114 111.5 322 | 3 txp437 115.8 323 | 3 sPb-6437 121.6 324 | 3 sPb-6121 121.6 325 | 3 sPb-6466 123.5 326 | 3 sPb-5655 123.5 327 | 3 sPb-5194 123.5 328 | 3 txs1927 126 329 | 3 txs422 126 330 | 3 BH245191 126.9 331 | 3 txp438 126.9 332 | 3 sPb-1480 126.9 333 | 3 txp440 126.9 334 | 3 txp439 127.3 335 | 3 sPb-8349 127.3 336 | 3 bnl15.20 127.3 337 | 3 txp441 127.3 338 | 3 bcd738 129 339 | 3 txp446 131.5 340 | 3 txp442 131.9 341 | 3 isu166 134.5 342 | 3 txp447 134.5 343 | 3 txs1226 135.4 344 | 3 txp421 137.4 345 | 3 txp38 137.4 346 | 3 txp448 138.7 347 | 3 txp449 140.1 348 | 3 sPb-9977 140.1 349 | 3 sPb-7186 140.1 350 | 3 txp422 140.1 351 | 3 txp420 141.4 352 | 3 sPb-4629 144.9 353 | 3 txp34 147.8 354 | 3 txp424 147.8 355 | 3 sPb-3228 149.9 356 | 3 sPb-6925 149.9 357 | 3 sPb-6649 150.6 358 | 3 isu68 150.6 359 | 3 txp427 158.2 360 | 3 txp69 158.2 361 | 3 txp425 162.1 362 | 3 CUa1 162.1 363 | 3 txp426 164.2 364 | 3 sPb-8802 172.3 365 | 4 txp506 0 366 | 4 txp504 6.1 367 | 4 sPb-9432 6.1 368 | 4 cdo962 6.1 369 | 4 isu47 6.1 370 | 4 sPb-2521 6.1 371 | 4 txs754 10.5 372 | 4 sPb-2739 10.5 373 | 4 umc111 16.8 374 | 4 sPb-4718 16.8 375 | 4 sPb-5826 16.8 376 | 4 sPb-4233 16.8 377 | 4 cup05 25.8 378 | 4 isu56 40.9 379 | 4 sPb-7256 48.7 380 | 4 sPb-1932 48.7 381 | 4 sPb-9580 53.3 382 | 4 sPb-6958 53.3 383 | 4 sPb-4851 71.4 384 | 4 sPb-1147 71.4 385 | 4 txp343 71.4 386 | 4 txp12 71.4 387 | 4 sPb-7534 75.8 388 | 4 sPb-6945 83.2 389 | 4 sPb-2313 83.2 390 | 4 txs1090 83.2 391 | 4 sPb-7216 95.7 392 | 4 sPb-9684 95.7 393 | 4 Sb5-214 97.1 394 | 4 isu42 97.1 395 | 4 cdo1380 97.1 396 | 4 sPb-0854 97.1 397 | 4 sPb-9713 97.1 398 | 4 sPb-2689 97.8 399 | 4 txs1072 97.8 400 | 4 sPb-3858 97.8 401 | 4 umc54 103.6 402 | 4 isu132 106.2 403 | 4 txp41 106.2 404 | 4 txp24 106.2 405 | 4 txp177 106.2 406 | 4 isu35 108 407 | 4 txp327 108 408 | 4 txs604 110.6 409 | 4 sPb-9568 110.6 410 | 4 txp60 120.7 411 | 4 txp51 128.5 412 | 4 txp212 128.5 413 | 4 txs1901 134.7 414 | 4 bcd1427 141.7 415 | 4 sPb-2832 141.7 416 | 4 txs1103 145 417 | 4 txp27 151.1 418 | 4 txp21 153 419 | 4 sPb-8255 155.5 420 | 4 sPb-1711 155.5 421 | 4 sPb-8218 156.1 422 | 4 sPb-4692 156.1 423 | 4 sPb-6098 156.1 424 | 4 sPb-7434 156.8 425 | 4 umc104 164.3 426 | 5 txs2031 0 427 | 5 sPb-0906 7 428 | 5 sPb-8186 8.9 429 | 5 txp65 14.4 430 | 5 sPb-0381 17.3 431 | 5 txp94 24.5 432 | 5 sPb-4313 24.5 433 | 5 sPb-8164 24.5 434 | 5 txs722 26.9 435 | 5 txp115 26.9 436 | 5 txs2072 26.9 437 | 5 txp30 28.9 438 | 5 sPb-9544 32.6 439 | 5 txp303 35.3 440 | 5 umc52 39.3 441 | 5 sPb-2752 44.1 442 | 5 sPb-1253 53.5 443 | 5 sPb-8306 53.5 444 | 5 sPb-1236 55.8 445 | 5 txp225 59.4 446 | 5 isu120 59.4 447 | 5 txp14 62.2 448 | 5 isu65 62.2 449 | 5 isu59 63.5 450 | 5 sPb-5892 63.5 451 | 5 sPb-1047 63.5 452 | 5 sPb-4806 63.5 453 | 5 sPb-5209 63.5 454 | 5 txs387 63.5 455 | 5 txp15 63.5 456 | 5 txp299 63.5 457 | 5 sPb-3845 63.5 458 | 5 txs713 63.5 459 | 5 sPb-0738 64.8 460 | 5 sPb-3766 64.8 461 | 5 sPb-3031 64.8 462 | 5 sPb-7030 73.2 463 | 5 sPb-3817 73.2 464 | 5 sPb-6323 73.2 465 | 5 sPb-7203 73.2 466 | 5 sPb-1454 74.6 467 | 5 txp23 75.9 468 | 5 sPb-1740 91.8 469 | 5 txp123 93.1 470 | 5 sPb-2570 93.1 471 | 5 sPb-9858 93.1 472 | 5 txp262 94.1 473 | 5 txp136 96.1 474 | 5 sPb-1396 104.4 475 | 5 sPb-0873 104.4 476 | 5 sPb-2874 104.4 477 | 5 sPb-1631 117.8 478 | 5 sPb-1794 118.5 479 | 6 sPb-1602 0 480 | 6 txp521 0 481 | 6 sPb-4902 0 482 | 6 sPb-1491 5.6 483 | 6 sPb-2551 17.9 484 | 6 txp6 31.6 485 | 6 cdo718 41.2 486 | 6 BG050402 46 487 | 6 isu142 52.4 488 | 6 sPb-8425 57.9 489 | 6 sPb-2457 57.9 490 | 6 sPb-3837 57.9 491 | 6 umc119 61.2 492 | 6 cup36 70.2 493 | 6 sPb-5030 73.5 494 | 6 isu138 76.8 495 | 6 sPb-2463 80 496 | 6 sPb-1543 85.2 497 | 6 txp145 86.7 498 | 6 umc53 88.4 499 | 6 txs1030 88.4 500 | 6 sPb-1572 89.1 501 | 6 isu58 90.5 502 | 6 txp265 90.5 503 | 6 txp317 90.5 504 | 6 txp274 90.9 505 | 6 txp219 90.9 506 | 6 txp104 90.9 507 | 6 txp97 92.9 508 | 6 sPb-7544 92.9 509 | 6 sPb-9752 92.9 510 | 6 umc34 93.8 511 | 6 txs1546 95.7 512 | 6 sPb-0188 97.9 513 | 6 sPb-8851 100.5 514 | 6 sPb-0255 109.3 515 | 6 txs1173 115.4 516 | 6 sPb-7753 115.4 517 | 6 txp484 115.4 518 | 6 txs1906 115.4 519 | 6 sPb-5230 115.4 520 | 6 bnl10.13.2 117 521 | 6 umc44 117 522 | 6 txp95 125.7 523 | 6 KS1 129.3 524 | 6 sPb-3962 132.8 525 | 6 txs1139 133.4 526 | 6 sPb-5403 134.1 527 | 6 txs1868 134.1 528 | 6 txp176 134.1 529 | 6 sPb-5765 135.5 530 | 6 sPb-7960 135.5 531 | 6 txp57 141 532 | 6 sPb-6526 144 533 | 6 sPb-9736 144 534 | 6 txs2063 144 535 | 6 isu147 145.6 536 | 6 txp17 145.6 537 | 6 sPb-5101 145.6 538 | 6 txs1533 146.4 539 | 6 txs1085 146.4 540 | 6 isu53 146.4 541 | 6 sPb-0477 146.4 542 | 6 sPb-3773 146.4 543 | 6 txs1124 146.4 544 | 6 cup37 146.4 545 | 6 sPb-1623 157.8 546 | 6 isu144 157.8 547 | 7 txp40 0 548 | 7 txp36 7.7 549 | 7 txp418 11.9 550 | 7 txs1674 11.9 551 | 7 sPb-2434 11.9 552 | 7 sPb-8980 14.5 553 | 7 isu139 16.4 554 | 7 txp417 17.7 555 | 7 txp413 18.5 556 | 7 txs1931 26.5 557 | 7 umc85 26.5 558 | 7 txp481 31.8 559 | 7 txp159 38.3 560 | 7 txs1096 44.1 561 | 7 txp312 46.6 562 | 7 sPb-6825 47.9 563 | 7 sPb-2566 54 564 | 7 isu38 54.7 565 | 7 txs1107 64.3 566 | 7 txp227 65.7 567 | 7 sPb-8296 65.7 568 | 7 bcd147 65.7 569 | 7 sPb-0460 67.2 570 | 7 sPb-0571 71.6 571 | 7 sPb-1244 71.6 572 | 7 gap342 71.6 573 | 7 sPb-1414 72.2 574 | 7 sPb-2757 72.2 575 | 7 sPb-7280 72.2 576 | 7 sPb-4874 72.8 577 | 7 sPb-6411 72.8 578 | 7 sPb-7678 72.8 579 | 7 sPb-7944 73.5 580 | 7 sPb-9508 73.5 581 | 7 txs1185.3 73.5 582 | 7 txp278 74.1 583 | 7 sPb-5796 75.6 584 | 7 sPb-6863 75.6 585 | 7 sPb-3541 75.6 586 | 7 sPb-7687 75.6 587 | 7 sPb-9206 75.6 588 | 7 sPb-7135 76.4 589 | 7 txs1563 76.4 590 | 7 umc23 80.5 591 | 7 txp92 87.8 592 | 7 sPb-9007 87.8 593 | 7 sPb-8251 95.9 594 | 7 sPb-6578 105 595 | 7 isu116 105 596 | 7 txs1579 113.6 597 | 7 txs1186 113.6 598 | 7 sPb-7086 118.6 599 | 7 sPb-3691 118.6 600 | 7 sPb-1012 118.6 601 | 7 sPb-5397 123.5 602 | 7 sPb-9365 123.5 603 | 7 txp295 123.5 604 | 7 cup52 126.2 605 | 7 sPb-7549 130.3 606 | 7 txs1554 130.3 607 | 7 txs1185.1 130.3 608 | 7 txs579 131.5 609 | 7 txp168 131.5 610 | 7 sPb-8608 132.8 611 | 8 txp273 0 612 | 8 sPb-9299 7.1 613 | 8 sPb-6589 15.5 614 | 8 sPb-1503 17 615 | 8 bnl3.04 22.4 616 | 8 sPb-7126 28.7 617 | 8 sPb-9818 28.7 618 | 8 sPb-7843 29.4 619 | 8 sPb-3536 29.4 620 | 8 sPb-3396 29.4 621 | 8 txp47 32.1 622 | 8 sPb-3247 40.3 623 | 8 sPb-8260 40.3 624 | 8 txs1185.2 46.2 625 | 8 txp520 46.2 626 | 8 cup47 53.4 627 | 8 isu54 57.6 628 | 8 sPb-8081 57.6 629 | 8 sPb-7027 57.6 630 | 8 sPb-0235 58.2 631 | 8 sPb-0239 58.2 632 | 8 sPb-0645 58.2 633 | 8 txs1440 66.6 634 | 8 sPb-4934 66.6 635 | 8 sPb-4432 66.6 636 | 8 sPb-5746 66.6 637 | 8 umc130 66.6 638 | 8 umc18 66.6 639 | 8 sPb-0380 66.6 640 | 8 sPb-6537 66.6 641 | 8 txs2068 66.6 642 | 8 sPb-0599 66.6 643 | 8 txs1379 66.6 644 | 8 txp210 66.6 645 | 8 rz261 67.6 646 | 8 sPb-0105 67.6 647 | 8 txp292 67.6 648 | 8 txp294 69.3 649 | 8 sPb-9700 72.7 650 | 8 sPb-0258 73.4 651 | 8 txp354 73.4 652 | 8 sPb-7889 74 653 | 8 cdo459 76.1 654 | 8 txs2065 79.6 655 | 8 sPb-1433 79.6 656 | 8 cdo344.1 79.6 657 | 8 sPb-4144 79.6 658 | 8 sPb-0856 80.2 659 | 8 sPb-7312 82.1 660 | 8 txp18 86.8 661 | 8 sPb-7375 86.8 662 | 8 txp516 86.8 663 | 8 sPb-1881 86.8 664 | 8 txp321 86.8 665 | 8 txs560 88.5 666 | 8 txp250 88.5 667 | 8 txs1220 89.7 668 | 8 txp105 97.7 669 | 8 sPb-7823 104.3 670 | 8 sPb-0159 104.3 671 | 8 txs1294 104.3 672 | 8 isu146 109.2 673 | 8 sPb-2846 109.2 674 | 8 sPb-4073 109.2 675 | 8 sPb-8931 109.2 676 | 8 sPb-7369 109.2 677 | 8 sPb-9584 109.2 678 | 8 BH245773 110.9 679 | 8 cup31 110.9 680 | 8 gap34 110.9 681 | 8 txs1353 110.9 682 | 8 sPb-0289 111.6 683 | 8 sPb-8993 116.5 684 | 9 isu140 0 685 | 9 umc134 0 686 | 9 cdo393 0 687 | 9 cdo542 0 688 | 9 sPb-6678 8 689 | 9 sPb-4522 8 690 | 9 txs1102 8 691 | 9 rz206 8 692 | 9 umc132 22.4 693 | 9 sPb-8716 22.4 694 | 9 cdo89 35.9 695 | 9 umc38 35.9 696 | 9 txp339 35.9 697 | 9 sPb-2670 43 698 | 9 sPb-6403 43 699 | 9 sPb-8319 43 700 | 9 sPb-9989 56.3 701 | 9 bcd454 63.9 702 | 9 sPb-1324 63.9 703 | 9 dhn2 63.9 704 | 9 sPb-8873 63.9 705 | 9 sPb-6852 63.9 706 | 9 sPb-4786 63.9 707 | 9 txs1383 63.9 708 | 9 sPb-9306 67.2 709 | 9 txp10 67.2 710 | 9 txs943 67.2 711 | 9 AW679255 67.2 712 | 9 cup02 67.2 713 | 9 sPb-9158 67.2 714 | 9 sPb-0326 75.8 715 | 9 sPb-5887 75.8 716 | 9 sBAC120 75.8 717 | 9 txp67 87.9 718 | 9 txp230 90.1 719 | 9 txp287 90.1 720 | 9 txp258 90.1 721 | 9 txp411 95.1 722 | 9 umc64 104.6 723 | 9 txp412 105.5 724 | 9 txs1150 105.5 725 | 9 cdo580 110.4 726 | 9 txp410 110.4 727 | 9 txp459 120.4 728 | 9 sPb-8368 123.1 729 | 9 sPb-7955 123.1 730 | 9 sPb-9091 123.1 731 | 9 sPb-7367 123.1 732 | 9 txp358 123.1 733 | 9 txs1703 123.1 734 | 9 rz390 128.9 735 | 9 umc12 128.9 736 | 9 txp289 132.3 737 | 10 sPb-2041 0 738 | 10 sPb-4129 4.4 739 | 10 sPb-0600 4.4 740 | 10 cdo590 6.5 741 | 10 AP2 9.4 742 | 10 sPb-4427 10.65 743 | 10 txs1078 11.9 744 | 10 AD12 13.1 745 | 10 cdo475 14.3 746 | 10 txs1163 22.2 747 | 10 isu136 32.9 748 | 10 sPb-0088 34.35 749 | 10 umc113 35.8 750 | 10 sPb-5005 62.6 751 | 10 sPb-3432 36.4 752 | 10 sPb-2193 36.4 753 | 10 sPb-0248 36.4 754 | 10 txs558 43.9 755 | 10 umc110 47.9 756 | 10 sPb-7473 51.2 757 | 10 cdo17 55.5 758 | 10 sPb-0668 57.85 759 | 10 umc21 55.75 760 | 10 txp20 53.7 761 | 10 txp309 55.75 762 | 10 txp331 56 763 | 10 txp270 55.75 764 | 10 bnl7.24 57.4 765 | 10 txp217 57.85 766 | 10 sPb-7173 58.7 767 | 10 txs1923 57.85 768 | 10 txp130 57.85 769 | 10 txs1929 57.85 770 | 10 sPb-4521 52 771 | 10 sPb-1938 59.65 772 | 10 txs1106 59.65 773 | 10 sPb-3696 61 774 | 10 sPb-5834 61.7 775 | 10 rz476 62.4 776 | 10 txs443 150.9 777 | 10 bnl5.04 68.7 778 | 10 gap1 78.3 779 | 10 cdo400 79.7 780 | 10 umc150 86.7 781 | 10 sPb-0859 87.55 782 | 10 txs236 87.55 783 | 10 txs1684 88.4 784 | 10 umc218 89.3 785 | 10 sPb-4539 91.3 786 | 10 txs758 102.6 787 | 10 txp141 105.6 788 | 10 isu162 108.6 789 | 10 cup43 103.1 790 | 10 isu45 109.2 791 | 10 txs664 109.8 792 | 10 cup07 115.2 793 | -------------------------------------------------------------------------------- /DATA/WHEAT_MACAF/CLEAN/Kofa_svevo: -------------------------------------------------------------------------------- 1 | real wmc95.1A X0 2 | 1A wmc24-1A 5.6 3 | 1A gwm772-1A 8.2 4 | 1A wmc469-1A 20.9 5 | 1A barc145-1A 21.2 6 | 1A gwm164-1A 21.7 7 | 1A barc158-1A 21.7 8 | 1A gwm1139-1A 22.5 9 | 1A barc287-1A 23.3 10 | 1B gwm550-1B 0 11 | 1B gwm33-1B 4.9 12 | 1B gwm374-1B 7.9 13 | 1B wmc85a-1B 8.2 14 | 1B barc128a-1B 12.8 15 | 1B gwm1100-1B 23.6 16 | 1B wmc500a-1B 28.1 17 | 1B wmc230-1B 31.3 18 | 1B barc8-1B 31.8 19 | 1B barc119-1B 33.3 20 | 1B gwm413-1B 36.5 21 | 1B cfd65-1B 42.1 22 | 1B gwm903-1B 42.9 23 | 1B gwm759-1B 47.3 24 | 1B gwm762-1B 47.6 25 | 1B gwm947-1B 47.8 26 | 1B ksum28-1B 50.3 27 | 1B gwm131-1B 55.6 28 | 1B barc181-1B 55.8 29 | 1B gpw93013-1B 90.7 30 | 1B barc61-1B 91 31 | 1B cfd48-1B 94.3 32 | 1B gwm806-1B 104.8 33 | 1B gwm153-1B 114 34 | 1B barc81-1B 114 35 | 1B gwm268-1B 115.5 36 | 1B gwm124-1B 117.5 37 | 1B barc188-1B 117.5 38 | 1B wmc59-1B 167.5 39 | 1B gwm1355-1B 168.4 40 | 1B barc80-1B 168.6 41 | 1B wmc728-1B 168.6 42 | 2A Ppd-A1-2A 0 43 | 2A cfa2201-2A 2.5 44 | 2A wmc522-2A 18 45 | 2A gwm1115-2A 21.6 46 | 2A gwm122-2A 24.3 47 | 2A gwm275-2A 28.6 48 | 2A gwm1045-2A 36.9 49 | 2B gwm429a-2B 0 50 | 2B gwm410-2B 0.6 51 | 2B gwm148-2B 1.9 52 | 2B gwm128-2B 2.1 53 | 2B barc55-2B 7 54 | 2B barc40-2B 7 55 | 2B barc101-2B 42 56 | 2B gwm1249-2B 44.4 57 | 2B wmc175-2B 46 58 | 2B gwm47-2B 49.7 59 | 2B gwm877-2B 51.3 60 | 2B gwm4947-2B 56.6 61 | 2B gwm1300-2B 57.2 62 | 2B wmc332-2B 62 63 | 2B gpw4043-2B 63.2 64 | 2B gwm1070-2B 65.6 65 | 2B gwm1354-2B 90.3 66 | 2B gwm4020-2B 93.8 67 | 2B gwm4888-2B 94 68 | 2B gwm1399-2B 96.3 69 | 2B CK162551-2B 102.1 70 | 2B gwm4110-2B 102.8 71 | 2B gwm1027-2B 102.8 72 | 2B gpw4474-2B 102.8 73 | 2B gwm1325-2B 102.8 74 | 2B CJ577969-2B 103.1 75 | 2B gwm846-2B 103.8 76 | 2B BG313362-2B 104.1 77 | 2B gwm4828-2B 105.6 78 | 2B BF474250-2B 112.4 79 | 2B wmc361-2B 112.9 80 | 3A barc86-3A 0 81 | 3A gwm779a-3A 1.5 82 | 3A gwm1620-3A 17.3 83 | 3A gwm133-3A 21.4 84 | 3A gwm1159a-3A 38.9 85 | 3A gwm10-3A 39.4 86 | 3B cfp3236-3B 0 87 | 3B gwm389-3B 0 88 | 3B cfp132-3B 2.1 89 | 3B gwm1034-3B 2.8 90 | 3B cft5057-3B 3.9 91 | 3B cft5014-3B 3.9 92 | 3B cft5005-3B 5.7 93 | 3B cft5036-3B 5.7 94 | 3B cft5000-3B 5.7 95 | 3B cft5006-3B 5.7 96 | 3B cft5021-3B 6.2 97 | 3B cft5034a-3B 6.2 98 | 3B cft5055-3B 6.2 99 | 3B cft5008-3B 6.2 100 | 3B barc133-3B 7.8 101 | 3B cfb6008-3B 9.8 102 | 3B umn10-3B 9.8 103 | 3B cfb6011-3B 9.8 104 | 3B cfb6060-3B 10.6 105 | 3B cfb6018-3B 10.8 106 | 3B cfp5075-3B 10.8 107 | 3B cfb6057-3B 11.9 108 | 3B cfb6116-3B 11.9 109 | 3B cfb6127-3B 11.9 110 | 3B cfb6058-3B 11.9 111 | 3B cfp5080-3B 11.9 112 | 3B cfb6059-3B 12.1 113 | 3B cfb6045-3B 12.1 114 | 3B cfb6043-3B 12.3 115 | 3B cfb6128-3B 12.3 116 | 3B cfp5078-3B 12.3 117 | 3B cfb6021-3B 13.1 118 | 3B cfp78-3B 15 119 | 3B cfp60-3B 15 120 | 3B cs-ssr7-3B 15.3 121 | 3B gwm493-3B 15.6 122 | 3B gpw7774-3B 19.5 123 | 3B barc102b-3B 38.6 124 | 3B barc218-3B 39.4 125 | 3B gwm779-3B 39.4 126 | 3B wmc505-3B 40.9 127 | 3B gwm420-3B 48.9 128 | 3B cfd4-3B 50 129 | 3B gwm685b-3B 50.8 130 | 3B barc203-3B 66.2 131 | 3B barc164-3B 79.5 132 | 3B gwm131-3B 88 133 | 3B gwm802-3B 92.5 134 | 3B barc115a-3B 100.4 135 | 3B ksum157-3B 113.6 136 | 3B barc84a-3B 127.5 137 | 3B wmc326-3B 129.3 138 | 4A gwm894-4A 0 139 | 4A gwm265-4A 8.7 140 | 4A wmc468-4A 9.5 141 | 4A wmc336-4A 9.5 142 | 4A wmc283-4A 11.1 143 | 4A wmc262-4A 37.6 144 | 4A cfd31a-4A 39.6 145 | 4A gwm1694-4A 63 146 | 4A barc78a-4A 65.4 147 | 4A wmc219-4A 67.9 148 | 4A wmc313-4A 69 149 | 4B wmc710-4B 0 150 | 4B barc193-4B 0.2 151 | 4B gwm1278-4B 11 152 | 4B gwm368-4B 18.4 153 | 4B wmc48-4B 22.6 154 | 4B gwm1084-4B 22.6 155 | 5A barc165a-5A 0 156 | 5A barc360a-5A 7.8 157 | 5A barc141-5A 9.8 158 | 5A gwm1236-5A 25.4 159 | 5A barc197-5A 25.8 160 | 5A ksum024-5A 25.8 161 | 5A gwm1570a-5A 29.6 162 | 5A cfa2141a-5A 48 163 | 5B barc128a-5B 0 164 | 5B gwm843-5B 0.3 165 | 5B barc74-5B 4 166 | 5B gwm213-5B 5.1 167 | 5B gwm335-5B 5.9 168 | 5B dupw205b-5B 14.4 169 | 5B gwm371-5B 16.9 170 | 5B wmc537-5B 18.1 171 | 5B cfd7-5B 85.2 172 | 5B gwm408a-5B 91.3 173 | 5B barc232-5B 93 174 | 5B barc142-5B 93 175 | 6A gwm1009-6A 0 176 | 6A gwm1296-6A 9.9 177 | 6A barc37-6A 21.3 178 | 6A gwm786a-6A 27.6 179 | 6A gwm356-6A 32.8 180 | 6A barc1077-6A 35.7 181 | 6A gwm1150-6A 57.2 182 | 6A gwm1017-6A 75.2 183 | 6A gwm1089-6A 91.8 184 | 6B dupw217-6B 0 185 | 6B gwm613-6B 2.6 186 | 6B wmc494-6B 52.6 187 | 6B barc14-6B 54.1 188 | 6B gwm518-6B 54.1 189 | 6B barc1169-6B 58.3 190 | 6B barc198-6B 67.4 191 | 6B gwm88-6B 67.4 192 | 6B gwm816-6B 67.7 193 | 6B gwm193-6B 70 194 | 6B cnl64-6B 70.6 195 | 6B gwm107-6B 72.8 196 | 6B gwm1682-6B 117.8 197 | 6B barc24-6B 118.5 198 | 6B gwm889-6B 127.6 199 | 6B gwm219-6B 129.9 200 | 6B barc134-6B 153.9 201 | 6B gwm1486-6B 156.6 202 | 6B wmc621-6B 160.5 203 | 7A wmc479a-7A 0 204 | 7A gwm1187-7A 0 205 | 7A wmc179-7A 0.5 206 | 7A cfa2028-7A 3.5 207 | 7A barc174-7A 24.6 208 | 7A gwm1065-7A 25.3 209 | 7A wmc65-7A 51.3 210 | 7A gwm4-7A 61 211 | 7A barc29-7A 65.3 212 | 7A gwm276-7A 79.8 213 | 7A cfa2123a-7A 81.8 214 | 7B gwm569-7B 0 215 | 7B barc1005b-7B 7.8 216 | 7B gwm400-7B 19.6 217 | 7B gwm573-7B 33.2 218 | 7B wmc182-7B 33.2 219 | 7B gwm1184-7B 41.5 220 | 7B gwm3019-7B 45 221 | 7B wmc546-7B 46 222 | 7B gwm46-7B 52.1 223 | 7B barc72a-7B 56.6 224 | 7B gwm297-7B 58.3 225 | 7B gwm871-7B 59.2 226 | 7B gwm1173-7B 59.9 227 | 7B gwm333-7B 67.6 228 | 7B gwm897-7B 68.1 229 | 7B wmc396-7B 77.5 230 | 7B barc176-7B 77.7 231 | 7B gwm767-7B 112.7 232 | 7B wmc517-7B 114 233 | 7B gwm611-7B 159 234 | 7B gwm783-7B 159.5 235 | 7B wmc273-7B 165.4 236 | 7B cfa2040a-7B 165.4 237 | 7B barc32a-7B 167 238 | 7B barc50-7B 169.3 239 | 7B barc182-7B 171.1 240 | 7B Psyb-B1-7B 174.3 241 | 7B gwm146-7B 175.5 242 | 7B ubw18b-7B 177.9 243 | 7B cfa2257-7B 179 244 | -------------------------------------------------------------------------------- /DATA/WHEAT_MACAF/CLEAN/Langdon_G1816: -------------------------------------------------------------------------------- 1 | real MARKER CM_.Haldane. 2 | 1A wPt-9757-1A 0 3 | 1A gwm357-1A 6.8 4 | 1A wmc333-1A 17.9 5 | 1A gwm1111-1A 21.1 6 | 1A gwm752b-1A 29.8 7 | 1A gwm691-1A 32.7 8 | 1A cfa2158A-1A 38.8 9 | 1A gwm3083-1A 42.3 10 | 1A wPt-4666-1A 47.8 11 | 1A wPt-0512-1A 54.9 12 | 1A gwm905-1A 60.6 13 | 1A gwm750-1A 60.6 14 | 1A wPt-11572-1A 64 15 | 1A wPt-4399-1A 74.4 16 | 1A wmc254-1A 76.5 17 | 1A 306333-1A 94.7 18 | 1A wPt-11558-1A 107.7 19 | 1B wPt-0983-1B 0 20 | 1B wPt-0308-1B 1.1 21 | 1B barc128b-1B 5 22 | 1B wPt-0655-1B 10.8 23 | 1B wPt-1139-1B 15 24 | 1B cfa2158b-1B 22.9 25 | 1B gwm752-1B 36.3 26 | 1B gwm413-1B 40.2 27 | 1B wPt-2694-1B 42.7 28 | 1B wPt-11532-1B 44.8 29 | 1B wPt-9283-1B 45.9 30 | 1B gwm18-1B 49.7 31 | 1B gwm926-1B 51.7 32 | 1B gwm1120-1B 54.6 33 | 1B gwm947-1B 56.9 34 | 1B wPt-3451-1B 66.6 35 | 1B wPt-11513-1B 73.6 36 | 1B wPt-6042-1B 75.6 37 | 1B wPt-6690-1B 77.3 38 | 1B wPt-0506-1B 79.6 39 | 1B gwm806-1B 85.8 40 | 1B wPt-11517-1B 135.8 41 | 1B wPt-1973-1B 136.4 42 | 1B gwm818-1B 137.9 43 | 2A gwm1176-2A 0 44 | 2A wPt-3611-2A 0.4 45 | 2A wPt-6245-2A 1.5 46 | 2A gwm339-2A 1.5 47 | 2A gwm275-2A 2.3 48 | 2A gwm95a-2A 4.9 49 | 2A gwm372-2A 5.9 50 | 2A gwm473-2A 9 51 | 2A wPt-8216-2A 10.2 52 | 2A gwm1054-2A 37 53 | 2A gwm445-2A 52.4 54 | 2A wPt-8427-2A 57.8 55 | 2A tPt-3136-2A 71.9 56 | 2A wPt-4855-2A 83 57 | 2A gwm1256-2A 101 58 | 2A gwm526-2A 106.5 59 | 2A ksum193-2A 115.9 60 | 2A wPt-6064-2A 122.5 61 | 2B gwm614-2B 0 62 | 2B wPt-3592-2B 0.8 63 | 2B wPt-11501-2B 11.3 64 | 2B wPt-8404-2B 19.4 65 | 2B wPt-4664-2B 23.8 66 | 2B wPt-11518-2B 25.4 67 | 2B wPt-7932-2B 29.1 68 | 2B wPt-8097-2B 30.5 69 | 2B wPt-6199-2B 44.7 70 | 2B wPt-7757-2B 46 71 | 2B wPt-1064-2B 49.6 72 | 2B gwm410-2B 51.1 73 | 2B gwm374-2B 66 74 | 2B gwm1177-2B 70 75 | 2B gwm1249-2B 88.4 76 | 2B wPt-11586-2B 95.9 77 | 2B wPt-11561-2B 97.6 78 | 2B wPt-1294-2B 99.2 79 | 2B wPt-0489-2B 100.6 80 | 2B wPt-0694-2B 107.4 81 | 2B wPt-6894-2B 130.2 82 | 2B wPt-2929a-2B 139.4 83 | 2B wPt-6643-2B 146.7 84 | 2B wPt-2135-2B 150.4 85 | 2B wPt-6522-2B 154.1 86 | 2B gwm4828-2B 155.1 87 | 3A gwm32a-3A 0 88 | 3A gwm133-3A 0.8 89 | 3A gwm720-3A 2.8 90 | 3A gwm1159b-3A 11.1 91 | 3A gwm1121-3A 13.5 92 | 3A wPt-8104-3A 13.9 93 | 3A wPt-1092-3A 65.3 94 | 3A gwm155a-3A 73 95 | 3A gwm1217-3A 74.3 96 | 3A wPt-5173-3A 80.8 97 | 3A wPt-1339-3A 99.4 98 | 3A wPt-1888-3A 103 99 | 3A wPt-4545-3A 103.9 100 | 3A wPt-9160-3A 110.5 101 | 3A gwm1229-3A 111.6 102 | 3B gwm389-3B 0 103 | 3B gwm493-3B 1.9 104 | 3B wPt-0267-3B 10.4 105 | 3B wPt-8828-3B 16.6 106 | 3B wPt-0250-3B 18.7 107 | 3B wPt-2830-3B 21 108 | 3B wPt-2766-3B 22.3 109 | 3B wPt-1612-3B 27.6 110 | 3B wPt-6945-3B 38.2 111 | 3B wPt-11570-3B 44.3 112 | 3B gwm685a-3B 47.4 113 | 3B gwm77-3B 48.3 114 | 3B gwm376-3B 50.4 115 | 3B gwm1015-3B 51.7 116 | 3B gwm1029-3B 63.2 117 | 3B gwm1005-3B 64.4 118 | 3B gwm853-3B 109.4 119 | 3B wPt-0384-3B 110.2 120 | 3B wPt-9577-3B 112.6 121 | 3B wPt-9049-3B 120.8 122 | 3B gwm705-3B 122.5 123 | 3B wPt-2491-3B 134 124 | 3B wPt-8363-3B 138.7 125 | 3B wPt-8752-3B 140.1 126 | 3B wPt-4194-3B 144.7 127 | 3B wPt-5943-3B 149.6 128 | 3B gwm1266-3B 153.8 129 | 3B wPt-4991-3B 159 130 | 3B wPt-0665-3B 161.8 131 | 3B wPt-1151-3B 166.3 132 | 3B gwm181-3B 167.2 133 | 4A gwm1093a-4A 0 134 | 4A gwm695-4A 10.6 135 | 4A gwm610-4A 13.5 136 | 4A dupw4-4A 30.4 137 | 4A gwm265-4A 39.6 138 | 4A gwm637-4A 40.8 139 | 4A wPt-6515-4A 51.1 140 | 4A wPt-7558-4A 62.1 141 | 4A wPt-11573-4A 65.5 142 | 4A wmc262-4A 77.5 143 | 4A gwm1251-4A 81.2 144 | 4A wPt-2129-4A 86.6 145 | 4A wPt-4596-4A 92.1 146 | 4A wPt-0538-4A 95.4 147 | 4A wPt-7821-4A 98.1 148 | 4A wPt-3449-4A 101.9 149 | 4A wPt-7354-4A 102.7 150 | 4A wPt-8657-4A 104.8 151 | 4B wPt-11509-4B 0 152 | 4B wPt-8555-4B 0 153 | 4B wPt-8892-4B 0 154 | 4B gwm3072-4B 1.6 155 | 4B gwm857-4B 4.9 156 | 4B gwm513a-4B 8.1 157 | 4B gwm781-4B 11.6 158 | 4B gwm251-4B 16.8 159 | 4B wPt6209-4B 20.5 160 | 4B wPt-3255-4B 29.6 161 | 4B gwm930-4B 33.3 162 | 4B gwm538-4B 35.2 163 | 5A gwm293-5A 0 164 | 5A gwm156-5A 13 165 | 5A gwm186-5A 20.6 166 | 5A gwm1236-5A 24.3 167 | 5A wmc415a-5A 39.3 168 | 5A wPt-11526-5A 42.2 169 | 5B gwm234a-5B 0 170 | 5B gwm443-5B 0.8 171 | 5B rPt-6127-5B 7.3 172 | 5B gwm1180-5B 20.2 173 | 5B wPt-1951-5B 24.2 174 | 5B wPt-4677-5B 26.1 175 | 5B barc128a-5B 28.4 176 | 5B gwm1108-5B 33.7 177 | 5B gwm371-5B 39.4 178 | 5B gwm831-5B 44.9 179 | 5B gwm499a-5B 48.3 180 | 5B wmc415-5B 58.1 181 | 5B tPt-3719-5B 67.4 182 | 5B wPt-6022-5B 68.3 183 | 5B wPt-3661-5B 74.8 184 | 5B gwm1043-5B 77.3 185 | 5B gwm777-5B 82.2 186 | 5B wPt-0498-5B 87 187 | 5B wPt-1733-5B 95.8 188 | 5B gwm408b-5B 100.2 189 | 5B wPt-5896a-5B 102.6 190 | 5B wPt-11579-5B 126.6 191 | 5B wPt-6880-5B 131.6 192 | 5B wPt-3213-5B 138.9 193 | 5B gwm1016-5B 144.4 194 | 5B wPt-7400-5B 160.9 195 | 5B wPt-11578-5B 165.6 196 | 5B wPt-0837-5B 166 197 | 6A gwm719-6A 0 198 | 6A gwm1573-6A 12.5 199 | 6A gwm786b-6A 43.1 200 | 6A gwm4675-6A 45.1 201 | 6A wPt-7572-6A 92.8 202 | 6A gwm1089-6A 110.8 203 | 6A wPt-3650-6A 117.5 204 | 6A wPt-9000-6A 120.2 205 | 6B wPt-6293-6B 0 206 | 6B wPt-8641-6B 0.8 207 | 6B wPt-1437-6B 12.5 208 | 6B wPt-4900-6B 12.5 209 | 6B wPt-5256-6B 22.7 210 | 6B wPt-8153-6B 27.1 211 | 6B wPt-11542-6B 30.5 212 | 6B wPt-0554a-6B 31.9 213 | 6B wPt-11554-6B 33.4 214 | 6B gwm390-6B 38.9 215 | 6B gwm768-6B 44.1 216 | 6B wPt5333-6B 47.4 217 | 6B wPt-9971-6B 49.7 218 | 6B wPt-11560-6B 52.6 219 | 6B wPt-7846-6B 54.7 220 | 6B gwm518-6B 60.5 221 | 6B barc136-6B 67.3 222 | 6B gwm361-6B 72.3 223 | 6B gwm193-6B 72.3 224 | 6B gwm771-6B 73.4 225 | 6B wPt-7935b-6B 75.9 226 | 6B wPt-11556-6B 79.5 227 | 6B gwm907-6B 90.5 228 | 6B gwm1016-6B 95.3 229 | 6B wPt-8554-6B 100.1 230 | 6B gwm1076-6B 108.3 231 | 6B gwm219-6B 112.1 232 | 6B wPt-1890-6B 135.2 233 | 6B wPt-2162-6B 140.3 234 | 6B wPt-0696-6B 142 235 | 6B wPt-4560-6B 142.9 236 | 6B tPt-9048-6B 142.9 237 | 7A wPt-1163-7A 0 238 | 7A wPt-7214-7A 2.6 239 | 7A wPt-9926-7A 5 240 | 7A wPt-11547-7A 5 241 | 7A gwm871a-7A 8.1 242 | 7A gwm1083-7A 10 243 | 7A wPt-9555-7A 26.5 244 | 7A wPt-11507-7A 30.1 245 | 7A gwm332-7A 49.3 246 | 7A gwm942-7A 67.5 247 | 7A wPt-11553-7A 70.2 248 | 7A gwm1061a-7A 75 249 | 7A gwm698-7A 75 250 | 7B gwm537-7B 0 251 | 7B gwm400-7B 2.8 252 | 7B gwm951-7B 8.9 253 | 7B gwm46-7B 19.8 254 | 7B wPt-2737-7B 25.2 255 | 7B wPt-11565-7B 27.1 256 | 7B gwm983-7B 37.8 257 | 7B wPt-2305-7B 52.2 258 | 7B wPt-3730-7B 55 259 | 7B wPt-11511-7B 56.9 260 | 7B wPt-8417a-7B 56.9 261 | 7B wPt-8233-7B 61.6 262 | 7B wPt-6104a-7B 67.7 263 | 7B wPt-7295a-7B 67.7 264 | 7B cfa2040a-7B 76.1 265 | 7B wPt-5228b-7B 84.4 266 | 7B wPt-11540-7B 85.2 267 | 7B wPt-0465a-7B 89 268 | 7B wPt-8920-7B 89 269 | 7B gwm263-7B 89 270 | -------------------------------------------------------------------------------- /LEGEND/legend_sheet1.txt: -------------------------------------------------------------------------------- 1 | 1@Select Dataset 2 | 2@The Genetic Map Comparator 3 | 3@The Genetic Map Comparator is a user friendly tool to compare and characterize genetic maps. To use is simply: 4 | 4@upload your maps: 5 | 5@... or select a demo dataset: 6 | 6@then explore your data using the top buttons to navigate through the proposed views 7 | 7@using the top buttons to navigate through the proposed views 8 | 8@project members: 9 | 9@other project members: 10 | -------------------------------------------------------------------------------- /LEGEND/legend_sheet2.txt: -------------------------------------------------------------------------------- 1 | 1@Summary Statistics 2 | 2@ - Key Statistics (several maps) - 3 | 3@Here you can compare various maps through five key statistics: 4 | 4@ 1. Number of genetic markers, 2. Overall size (in cM), 3. Average gap size between 2 of their markers (cM), 4. Size of their biggest gap (cM) and the number of unique positions. 5 | 5@ - Key Statistics (1 map) - 6 | 6@This table provides five key statistics for the chromosomes of the selected map: 1. their number of genetic markers, 2. their overall size (in cM), 3. the average gap size between 2 of their markers (cM), 4. the size of their biggest gap (cM) and the number of unique positions. This is the usual table you provide when doing a publication involving genetic maps. 7 | 7@ - Marker Density - 8 | 8@Here is a description of marker density along chromosomes. This figure allows to visualize chromosome length variation among maps, and to identify regions with unusually high/low genetic marker density. 9 | 9@Select some maps: 10 | 10@Select some chromosomes: 11 | 11@Select one map: 12 | 12@The donut plot (on the left) summarizes the selected statistic comparison, whereas the bar-plot provides details for every chromosome separately. 13 | 13@Select one feature -------------------------------------------------------------------------------- /LEGEND/legend_sheet3.txt: -------------------------------------------------------------------------------- 1 | 1@Compare Positions 2 | 2@ - Comparison of Marker Positions - 3 | 3@The main goal of this application is to compare genetic positions of markers from several genetic maps. 4 | 4@Select some maps: 5 | 5@Select one chromosome: 6 | 6@Select maps in the order you want to see them (each new selected map is added on the right). 7 | 7@Common markers between two adjacent maps are linked with lines. Hover markers to get their names. You can also zoom on a specific area, and slide along the Y axis (position in cM). Double-click to come back to the initial view. You can personalize the rendering by choosing line thickness and 8 | 8@The minimal subset of markers that would need to be removed to avoid crossing edges between 2 adjacent maps is detected and represented by grey lines. It is also possible to download the list of these markers. 9 | 9@ 10 | 10@ -------------------------------------------------------------------------------- /LEGEND/legend_sheet4.txt: -------------------------------------------------------------------------------- 1 | 1@Interchromosome Analysis 2 | 2@ - Interchromosome Analysis - 3 | 3@The comparison of marker positions can be observed for two maps. Selecting a single chromosome allows to visualize recombination rates between maps. Selecting all chromosomes allows to visualize interchromosome recombinations (i.e. markers not attributed to the same chromosome in the two maps) 4 | 4@Select the first map: 5 | 5@Select the second map: 6 | 6@Select chromosome(s): 7 | 7@ 8 | 8@ 9 | 9@ 10 | 10@ -------------------------------------------------------------------------------- /LEGEND/legend_sheet5.txt: -------------------------------------------------------------------------------- 1 | 1@Raw Data 2 | 2@This table shows the raw marker data (as read from your input file) for the selected map. You can sort rows (i.e. markers) according to i) chromosome/group location, ii) name or iii) position. 3 | 3@Select the reference map: 4 | 4@Select chromosome(s): 5 | 5@You can also search for a specific marker based on its name. 6 | 6@Marker Information and Filtering 7 | 7@It is also possible to remove markers from the analysis. If you want to do so, please type the name of a marker to remove (e.g: ’sPb-601’) or a pattern to remove using regular expressions (e.g. typing ’sPb.*’ will remove all markers starting by sPb). If you want to remove several markers/patterns, separate them with a comma (e.g: ’sPb-601,xbarc-213’). You can also decide to keep only some markers, selecting the ‘keep’ option. 8 | 8@ 9 | 9@ 10 | 10@ -------------------------------------------------------------------------------- /LEGEND/legend_sheet6.txt: -------------------------------------------------------------------------------- 1 | 1@Help 2 | 2@ - About - 3 | 3@The Genetic Map Comparator allows to easily compare several linear maps (genetic, physical, radiated hybrids). The first step is to upload your maps through the Select Dataset sheet. Three example datasets (from sorghum and wheat) are provided to easily explore the Genetic map comparator features. For a more in depth analysis of your genotyping data (He, FIS, MAF, ..), you may find 4 | 4@Once a dataset is uploaded you can explore it using the various sheets accessible via links at the top of the window. Each sheet helps you to study some specific features of your maps, they allow you to compare your maps via standard statistics (number of markers, average gap size between markers etc.) as well as to obtain a visual representation of your map congruences by plotting marker positions along chromosomes. 5 | 5@ - Input Files - 6 | 6@The Genetic map comparator is able to import map files produced by most commonly used software for linkage analysis (e.g. Carthagene and JoinMap). Each map must be stored in its own file and all data files stored together in one specific folder. Use the upload facility of the home page to browse your local folders and select desired maps using multiple file selection. The column name has no impact but must be ordered correctly (see details and example below) and be separated by either semi-colon (;) or tabulation (\t). Three formats are accepted: 7 | 7@ 8 | 8@ 9 | 9@ 10 | 10@ - Contact - 11 | 11@ - Run the Application Locally - 12 | 12@The Genetic Map Comparator is an R shiny application available online. Uploaded data are NOT kept on the Webserver. However, for confidentiality or better reactivity, it is also easy to run it locally following these steps: 13 | 13@ - 1/ 14 | 14@ - 2/ Install the R Shiny library. In the R console type the following commands: 15 | 15@ - 3/ Call the application from its Github repository typing this command in the R console: 16 | 16@ on your computer and open it. 17 | 17@Sharing published maps 18 | 18@Uploaded data are NOT kept on the Webserver, but if you want to share your published data with the community via this Genetic Map Comparator website, please do not hesitate to contact us. We will be glad to add your maps on the list of proposed maps displayed on the home page. 19 | 19@ 20 | 20@ 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | The Genetic Map Comparator 2 | =================== 3 | 4 | 5 | [**Tool working online here**](http://www.agap-sunshine.inra.fr/genmapcomp/) 6 | 7 | 8 | Overview 9 | -------- 10 | The Genetic Map Comparator is an R Shiny application made to compare genetic maps. 11 | You can use it trough the [**online version**](http://www.agap-sunshine.inra.fr/genmapcomp/) and read the related publication in [**bioinformatics**](https://academic.oup.com/bioinformatics/article-abstract/33/9/1387/2908431/The-genetic-map-comparator-a-user-friendly?redirectedFrom=fulltext). 12 | 13 | Once your dataset is uploaded you can explore it using the various sheets accessible via links at the top of the window. Each sheet helps you to study some specific features of your maps, they allow you to compare your maps via standard statistics (number of markers, average gap size between markers etc.) as well as to obtain a visual representation of your map congruences by plotting marker positions along chromosomes. 14 | 15 | Here is a screenshot of the main sheets of the app: 16 | ![fig1](RESSOURCES/Figure1.jpg) 17 | 18 | 19 | Input Format 20 | -------- 21 | The Genetic map comparator is able to import map files produced by most commonly used software for linkage analysis (e.g. [Carthagene](https://www.ncbi.nlm.nih.gov/pubmed/9322047) and [JoinMap](https://www.kyazma.nl/index.php/mc.JoinMap)). Each map must be stored in its own file and all data files stored together in one specific folder. Use the upload facility of the home page to browse your local folders and select desired maps using multiple file selection. The column name has no impact but must be ordered correctly (see details and example below) and be separated by either ‘;’ or tabulation (‘\t’). See an extensive description of the accepted inputs in the [help page](www.agap-sunshine.inra.fr/genmapcomp/) of the online application. 22 | 23 | Local use 24 | -------- 25 | For large datasets or privacy concerns, you can use the tool locally. 26 | You need R to be installed on your computer. 27 | Open R and use the code below: 28 | ``` 29 | install.packages("shiny") 30 | library(shiny) 31 | runGitHub("GenMap-Comparator","holtzy") 32 | ``` 33 | 34 | Local installation 35 | -------- 36 | Since local use shows problems for microsoft users, you can install the Genetic Map Comparator on your machine. 37 | 38 | **1.** Download the whole repository 39 | 40 | **2.** Open R and make sure these library are installed: 41 | ``` 42 | library(shiny) 43 | library(plotly) 44 | library(ggplot2) 45 | library(DT) 46 | library(shinythemes) 47 | library(shinyAce) 48 | library(RColorBrewer) 49 | library(qualV) 50 | library(colourpicker) 51 | ``` 52 | If you miss a library, remember you can install it with: 53 | ``` 54 | ìnstall.packages("shiny") 55 | ``` 56 | 57 | **3.** 58 | Then, set the working directory and run the App: 59 | ``` 60 | setwd("my/path/to/the/github/folder/you/downloaded") 61 | runApp() 62 | ``` 63 | 64 | Citing 65 | -------- 66 | If you find The Genetic Map Comparator useful, please cite: 67 | The Genetic Map Comparator: a user-friendly application to display and compare genetic maps. 68 | *Yan Holtz, Jacques David, Vincent Ranwez*, **[Bioinformatics (2017) 33(9): 1387–1388](https://www.ncbi.nlm.nih.gov/pubmed/28453680)** 69 | 70 | Members 71 | -------- 72 | The genetic map comparator has been developped by 3 researchers from [Montpellier Supagro](www.supagro.fr/): 73 | Yan Holtz: [homepage](https://holtzyan.wordpress.com/) 74 | Jacques David: [homepage](https://www.researchgate.net/profile/Jacques_David4) 75 | Vincent Ranwez: [homepage](https://sites.google.com/site/ranwez/) 76 | 77 | We would like to thank **Cédric Goby** for his help during the shiny server deployment and **François Lechevallier** for developing and maintaining the [website](http://bioweb.supagro.inra.fr/geneticMapComparator/) hosting this tool. 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | -------------------------------------------------------------------------------- /RESSOURCES/Figure1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/Figure1.jpg -------------------------------------------------------------------------------- /RESSOURCES/INRA_logo - copie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/INRA_logo - copie.jpg -------------------------------------------------------------------------------- /RESSOURCES/INRA_logo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/INRA_logo.jpg -------------------------------------------------------------------------------- /RESSOURCES/LOGO_genmapcomp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/LOGO_genmapcomp.png -------------------------------------------------------------------------------- /RESSOURCES/Logo_GenMap_Small.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/Logo_GenMap_Small.png -------------------------------------------------------------------------------- /RESSOURCES/Logo_supagro.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/Logo_supagro.jpg -------------------------------------------------------------------------------- /RESSOURCES/donut_function.R: -------------------------------------------------------------------------------- 1 | # The doughnut function permits to draw a donut plot 2 | doughnut <- 3 | function (x, labels = names(x), edges = 200, outer.radius = 0.8, 4 | inner.radius=0.6, clockwise = FALSE, 5 | init.angle = if (clockwise) 90 else 0, density = NULL, 6 | angle = 45, col = NULL, border = FALSE, lty = NULL, 7 | main = NULL, ...) 8 | { 9 | if (!is.numeric(x) || any(is.na(x) | x < 0)) 10 | stop("'x' values must be positive.") 11 | if (is.null(labels)) 12 | labels <- as.character(seq_along(x)) 13 | else labels <- as.graphicsAnnot(labels) 14 | x <- c(0, cumsum(x)/sum(x)) 15 | dx <- diff(x) 16 | nx <- length(dx) 17 | plot.new() 18 | pin <- par("pin") 19 | xlim <- ylim <- c(-1, 1) 20 | if (pin[1L] > pin[2L]) 21 | xlim <- (pin[1L]/pin[2L]) * xlim 22 | else ylim <- (pin[2L]/pin[1L]) * ylim 23 | plot.window(xlim, ylim, "", asp = 1) 24 | if (is.null(col)) 25 | col <- if (is.null(density)) 26 | palette() 27 | else par("fg") 28 | col <- rep(col, length.out = nx) 29 | border <- rep(border, length.out = nx) 30 | lty <- rep(lty, length.out = nx) 31 | angle <- rep(angle, length.out = nx) 32 | density <- rep(density, length.out = nx) 33 | twopi <- if (clockwise) 34 | -2 * pi 35 | else 2 * pi 36 | t2xy <- function(t, radius) { 37 | t2p <- twopi * t + init.angle * pi/180 38 | list(x = radius * cos(t2p), 39 | y = radius * sin(t2p)) 40 | } 41 | for (i in 1L:nx) { 42 | n <- max(2, floor(edges * dx[i])) 43 | P <- t2xy(seq.int(x[i], x[i + 1], length.out = n), 44 | outer.radius) 45 | polygon(c(P$x, 0), c(P$y, 0), density = density[i], 46 | angle = angle[i], border = border[i], 47 | col = col[i], lty = lty[i]) 48 | Pout <- t2xy(mean(x[i + 0:1]), outer.radius) 49 | lab <- as.character(labels[i]) 50 | if (!is.na(lab) && nzchar(lab)) { 51 | lines(c(1, 1.05) * Pout$x, c(1, 1.05) * Pout$y) 52 | text(1.1 * Pout$x, 1.1 * Pout$y, labels[i], 53 | xpd = TRUE, adj = ifelse(Pout$x < 0, 1, 0), 54 | ...) 55 | } 56 | ## Add white disc 57 | Pin <- t2xy(seq.int(0, 1, length.out = n*nx), 58 | inner.radius) 59 | polygon(Pin$x, Pin$y, density = density[i], 60 | angle = angle[i], border = border[i], 61 | col = "white", lty = lty[i]) 62 | } 63 | 64 | title(main = main, ...) 65 | invisible(NULL) 66 | } 67 | -------------------------------------------------------------------------------- /RESSOURCES/tmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/RESSOURCES/tmp -------------------------------------------------------------------------------- /global.R: -------------------------------------------------------------------------------- 1 | 2 | ################################################ 3 | # 4 | # THE GENETIC MAP COMPARATOR 5 | # 6 | ############################################### 7 | 8 | 9 | 10 | # ---- PART1 : Check that the currently-installed version of R is at least the minimum required version. 11 | R_min_version = "3.1" 12 | R_version = paste0(R.Version()$major, ".", R.Version()$minor) 13 | if(compareVersion(R_version, R_min_version) < 0){ 14 | stop("You do not have the latest required version of R installed.\n", 15 | "Launch should fail.\n", 16 | "Go to http://cran.r-project.org/ and update your version of R.") 17 | } 18 | 19 | 20 | 21 | # ----- PART2: Install basic required packages if not available/installed. 22 | install_missing_packages = function(pkg, version = NULL, verbose = TRUE){ 23 | availpacks = .packages(all.available = TRUE) 24 | #source("http://bioconductor.org/biocLite.R") 25 | missingPackage = FALSE 26 | if(!any(pkg %in% availpacks)){ 27 | if(verbose){ 28 | message("The following package is missing.\n", 29 | pkg, "\n", 30 | "Installation will be attempted...") 31 | } 32 | missingPackage <- TRUE 33 | } 34 | if(!is.null(version) & !missingPackage){ 35 | # version provided and package not missing, so compare. 36 | if( compareVersion(a = as.character(packageVersion(pkg)), 37 | b = version) < 0 ){ 38 | if(verbose){ 39 | message("Current version of package\n", 40 | pkg, "\t", 41 | packageVersion(pkg), "\n", 42 | "is less than required. 43 | Update will be attempted.") 44 | } 45 | missingPackage <- TRUE 46 | } 47 | } 48 | if(missingPackage){ 49 | #biocLite(i, suppressUpdates = TRUE) 50 | print(pkg) 51 | print(paste("---- installing a more recent version of",pkg,sep="")) 52 | install.packages(pkg, repos = "http://cran.r-project.org") } 53 | } 54 | 55 | 56 | # PART3: --- Define list of package names and required versions. 57 | deppkgs = c(shiny="0.14.2", plotly = "4.5.6", ggplot2 = "2.2.0", DT="0.2", shinythemes="1.1", shinyAce="0.2.1", RColorBrewer="1.1.2", qualV="0.3.2", colourpicker="0.2", gtools="3.5.0") 58 | 59 | # Loop on package check, install, update 60 | pkg1 = mapply(install_missing_packages, 61 | pkg = names(deppkgs), 62 | version = deppkgs, 63 | MoreArgs = list(verbose = TRUE), 64 | SIMPLIFY = FALSE, 65 | USE.NAMES = TRUE) 66 | 67 | 68 | 69 | ################################################################################ 70 | # Load packages that must be fully-loaded 71 | ################################################################################ 72 | for(i in names(deppkgs)){ 73 | library(i, character.only = TRUE) 74 | message(i, " package version:\n", packageVersion(i)) 75 | } 76 | ################################################################################ 77 | 78 | # In this file, I add all functions / file / parameters that are NOT reactive and that are common to ui.R and server.R 79 | # It is my global environment ! 80 | 81 | 82 | # == Check if libraries are available. install it if not. 83 | #getPckg <- function(pckg) install.packages(pckg, repos = "http://cran.r-project.org") 84 | #for(i in c("shiny","plotly","DT","RColorBrewer","shinyAce","shinythemes","qualV")){ 85 | # pckg = try(require(i, character.only = TRUE)) 86 | # if(!pckg) { 87 | # getPckg(i) 88 | #}} 89 | 90 | 91 | # == load Libraries 92 | #library(shiny) 93 | #library(plotly) 94 | #library(DT) 95 | #library(RColorBrewer) 96 | #library(shinyAce) 97 | #library(shinythemes) 98 | #library(qualV) 99 | 100 | # == Colors for the App : 101 | my_colors=brewer.pal( 12 , "Set3")[-2] 102 | 103 | # == Get the legends 104 | legend1=read.table("LEGEND/legend_sheet1.txt",sep="@")[,2] 105 | legend2=read.table("LEGEND/legend_sheet2.txt",sep="@")[,2] 106 | legend3=read.table("LEGEND/legend_sheet3.txt",sep="@")[,2] 107 | legend4=read.table("LEGEND/legend_sheet4.txt",sep="@")[,2] 108 | legend5=read.table("LEGEND/legend_sheet5.txt",sep="@")[,2] 109 | legend6=read.table("LEGEND/legend_sheet6.txt",sep="@")[,2] 110 | 111 | # == Functions 112 | # Donut plot 113 | source("RESSOURCES/donut_function.R") 114 | 115 | # == Set the size of the logo of partners 116 | grand=1.5 117 | 118 | -------------------------------------------------------------------------------- /server.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | ################################################ 4 | # 5 | # THE GENETIC MAP COMPARATOR 6 | # 7 | ############################################### 8 | 9 | 10 | 11 | 12 | shinyServer(function(input, output, session) { 13 | 14 | 15 | 16 | 17 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 18 | 19 | #----------------------------------------------------------------------------- 20 | # --- ALLOWS USER TO DOWNLOAD EXAMPLE DATASET 21 | #----------------------------------------------------------------------------- 22 | 23 | 24 | # format OneMap 25 | output$load_ex_format1 <- downloadHandler( 26 | filename = "GenMapComp_Example1.csv", 27 | content <- function(file) { 28 | file.copy("DATA/EX_HELP_PAGE/Example_Data_Set1.csv", file) 29 | } 30 | ) 31 | 32 | 33 | #format Mapchart 34 | output$load_ex_format2 <- downloadHandler( 35 | filename = "GenMapComp_Example2.csv", 36 | content <- function(file) { 37 | file.copy("DATA/EX_HELP_PAGE/Example_Data_Set2.csv", file) 38 | } 39 | ) 40 | 41 | #format Carthagène 42 | output$load_ex_format3 <- downloadHandler( 43 | filename = "GenMapComp_Example3.csv", 44 | content <- function(file) { 45 | file.copy("DATA/EX_HELP_PAGE/Example_Data_Set3.csv", file) 46 | } 47 | ) 48 | 49 | 50 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 61 | 62 | #----------------------------------------------------------------------------- 63 | # --- UPLOAD MAPS AND FILE FORMATING 64 | #----------------------------------------------------------------------------- 65 | 66 | 67 | 68 | 69 | # 0/ --- Selection of the data set: default dataset / Example dataset / Chosen dataset 70 | inFile=reactive({ 71 | 72 | # Cleaning 73 | rm(list=ls()) 74 | my_global_old_choice <- c(3) 75 | 76 | 77 | # If nothing is choosen I take the chosen exemple dataset 78 | if ( is.null(input$file1)) { 79 | 80 | # So no error message needed 81 | output$error_message<- renderUI({ helpText("") }) 82 | 83 | if( input$file2=="sorghum (Mace et al. 2009)" | is.null(input$file2)){ inFile=data.frame(name=as.character(c("CIRAD","S2","S4","S5","S6","TAMU")) , datapath=as.character(c("DATA/SORGHUM/CIRAD", "DATA/SORGHUM/S2" , "DATA/SORGHUM/S4" , "DATA/SORGHUM/S5" , "DATA/SORGHUM/S6" , "DATA/SORGHUM/TAMU" )) ) } 84 | else if( input$file2=="wheat (Maccaferri et al. 2015)"){ inFile=data.frame(name=as.character(c("Ben_Pi41025","Colosseo_Lloyd","Kofa_svevo","Langdon_G1816","Latino_MG5323","Mohawk_Cocorrit69","Simeto_Levante")) , datapath=as.character(c("DATA/WHEAT_MACAF/CLEAN/Ben_Pi41025", "DATA/WHEAT_MACAF/CLEAN/Colosseo_Lloyd" , "DATA/WHEAT_MACAF/CLEAN/Kofa_svevo", "DATA/WHEAT_MACAF/CLEAN/Langdon_G1816", "DATA/WHEAT_MACAF/CLEAN/Latino_MG5323", "DATA/WHEAT_MACAF/CLEAN/Mohawk_Cocorrit69", "DATA/WHEAT_MACAF/CLEAN/Simeto_Levante" )) ) } 85 | else if( input$file2=="wheat (Holtz et al. 2016)"){ inFile=data.frame(name=as.character(c("map_DS","map_DL","map_consensus","physical_position")) , datapath=as.character(c("DATA/WHEAT_TRAM/map_DS","DATA/WHEAT_TRAM/map_DL","DATA/WHEAT_TRAM/map_consensus","DATA/WHEAT_TRAM/physical_position" )) ) } 86 | 87 | 88 | # If the user proposes a dataset: 89 | }else{ 90 | 91 | # I have to check if the proposed fileS ARE readable and in the good format! 92 | mistake_presence=FALSE 93 | output$error_message<- renderUI({ helpText("") }) 94 | 95 | # I need at least 2 files 96 | if( length(input$file1$datapath)<2 ){ 97 | mistake_presence=TRUE 98 | output$error_message<- renderUI({ helpText("Please select at least 2 maps" , style="color:red ; font-family: 'times'; font-size:13pt") }) 99 | 100 | # If I have at least 2 maps, I check them one by one: 101 | }else{ 102 | for(i in c(input$file1$datapath)) { 103 | 104 | # I try to read the file 105 | a=try(read.table(i, header=T , dec="." ,na.strings="NA")) 106 | 107 | # if the file is NOT readable by R 108 | if(class(a)=="try-error"){ 109 | mistake_presence=TRUE 110 | output$error_message<- renderUI({ helpText("File input is not readable by R. Is it a genetic map?" , style="color:red ; font-family: 'times'; font-size:13pt") }) 111 | break 112 | 113 | # if the file does not have 2 or 3 columns 114 | }else{ 115 | if( !ncol(a)%in%c(1,2,3) & nrow(a)!=0 ){ 116 | mistake_presence=TRUE 117 | output$error_message<- renderUI({ helpText("One of your file does not have 2 nor 3 columns" , style="color:red ; font-family: 'times'; font-size:13pt") }) 118 | break 119 | }}} 120 | 121 | 122 | } 123 | 124 | # So if there is a mistake in the proposed files, I keep sorghum as an example. Else I take the proposed files 125 | if( mistake_presence==TRUE){ 126 | inFile=data.frame(name=as.character(c("CIRAD","S2","S4","S5","S6","TAMU")) , datapath=as.character(c("DATA/SORGHUM/CIRAD", "DATA/SORGHUM/S2" , "DATA/SORGHUM/S4" , "DATA/SORGHUM/S5" , "DATA/SORGHUM/S6" , "DATA/SORGHUM/TAMU" )) ) 127 | }else{ 128 | inFile <- input$file1 129 | } 130 | 131 | } 132 | 133 | }) 134 | 135 | # Check if it worked properly 136 | #observe({ print("Mon inFile") ; print ( inFile() ) ; print("--") }) 137 | 138 | 139 | 140 | 141 | 142 | 143 | # 1/ --- Catch the map names we have to compare : 144 | MY_map_files=reactive({ 145 | 146 | # List of map files: 147 | map_files=as.list(inFile()$name) 148 | # return this list, but do not forget to format it: 149 | return(as.character(unlist(map_files))) 150 | }) 151 | 152 | # Check if it worked properly 153 | #observe({ print("mes maps selectionnées") ; print ( MY_map_files()) ; print("test widget selection") ; selected=c(MY_map_files()[1],MY_map_files()[2]) ; print(selected) }) 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | # 2/ --- Load every maps and add their content in a list. 162 | MY_maps=reactive({ 163 | 164 | 165 | # I am reactive to the selection of input files ! 166 | inFile=inFile() 167 | 168 | # Read and format maps one by one, and add them to a list: 169 | my_maps=list() 170 | for(i in inFile$datapath){ 171 | 172 | # Load the map 173 | map_tmp=read.table(i , header=T , dec="." ,na.strings="NA") 174 | 175 | # If I have only 1 column, the separator was wrong, I try with ";": 176 | if(ncol(map_tmp)==1){ 177 | map_tmp=read.table(i , header=T , dec="." ,na.strings="NA" , sep=";") 178 | } 179 | 180 | # If I have 2 columns, It is the MAPCHART format --> I need to reformat it! 181 | if(ncol(map_tmp)==2){ 182 | junctions=c(1, as.numeric(row.names(map_tmp[map_tmp[,1]=="group" , ])), nrow(map_tmp)+1 ) 183 | nb_rep=junctions[-1] - junctions[-length(junctions)] 184 | LG_names=c(colnames(map_tmp)[2] , as.character(map_tmp[map_tmp[,1]=="group" , 2]) ) 185 | map_tmp$new=rep(LG_names , times=nb_rep) 186 | map_tmp=map_tmp[map_tmp[,1]!="group" , ] 187 | map_tmp=map_tmp[ , c(3,1,2)] 188 | } 189 | 190 | 191 | # If I have only 0 line, it is the CARTHAGENE Format --> I need to reformat it! 192 | if(nrow(map_tmp)==0){ 193 | tmp_data=read.table(i , header=F , sep=" ") 194 | tmp_data=apply(tmp_data, 2 , as.character) 195 | tmp_data=gsub("\\}" , "" , tmp_data ) 196 | tmp_data=tmp_data[-c(1,2)] 197 | map_tmp=data.frame(matrix(0, length(tmp_data), 3)) 198 | num=0 199 | for(k in tmp_data){ 200 | # If the LG changes, I change my "LG" variable, and num1 back to -2 201 | if( substr(k,1,1) == "{" ){ 202 | LG=gsub("\\{","",k) 203 | num1=-2 204 | } 205 | # For each step of the loop, num1 increases 206 | num1=num1+1 207 | # When I am not reading a LG name (num1=-2) or a likelihood (num1=0), I add stuff in my map_tmp table 208 | # The line number (num) increase only once every 2 iterations 209 | if( num1>0){ 210 | num=num+num1%%2 211 | map_tmp[num,1]=LG 212 | map_tmp[num,num1%%2+2]=k 213 | }} 214 | # Clean this map_tmp 215 | map_tmp=map_tmp[ c(1:num) , c(1,3,2)] 216 | } 217 | 218 | 219 | # Columns must be in the good format: 220 | map_tmp[,1]=as.factor(map_tmp[,1]) 221 | map_tmp[,2]=as.factor(map_tmp[,2]) 222 | map_tmp[,3]=as.numeric(as.character(map_tmp[,3])) 223 | 224 | # With the good names: 225 | colnames(map_tmp)=c("group","marker","position") 226 | 227 | # I keep only the first 3 columns (if they are more..) 228 | map_tmp=map_tmp[,c(1:3)] 229 | 230 | # I remove positions where an information is missing: 231 | map_tmp=na.omit(map_tmp) 232 | 233 | # And ordered 234 | map_tmp=map_tmp[order(map_tmp$group , map_tmp$position ) , ] 235 | 236 | # I remove markers if the user choosed to remove markers in the raw data sheet! 237 | if(input$keep_or_remove=="remove"){ 238 | list_mark_remove=unlist(strsplit(input$text_mark_remove, ",")) 239 | list_mark_remove=paste("^",list_mark_remove,"$",sep="") 240 | for(i in list_mark_remove){ 241 | map_tmp=map_tmp[!grepl(i,map_tmp$marker) , ] 242 | } 243 | } 244 | 245 | # I keep only some markers if the user choosed to keep markers in the raw data sheet! 246 | if(input$keep_or_remove=="keep"){ 247 | list_mark_keep=unlist(strsplit(input$text_mark_remove, ",")) 248 | list_mark_keep=paste("^",list_mark_keep,"$",sep="") 249 | for(i in list_mark_keep){ 250 | map_tmp=map_tmp[grepl(i,map_tmp$marker) , ] 251 | } 252 | } 253 | 254 | # Add it to the list 255 | my_maps[[length(my_maps)+1]]=map_tmp 256 | 257 | } 258 | 259 | return(my_maps) 260 | 261 | }) 262 | 263 | # Check everything worked properly 264 | #observe({ print("summary de la carte 1:") ; print ( head(MY_maps()[[1]]) ) }) 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | # 3/ --- Merge the maps together 274 | MY_data=reactive({ 275 | 276 | # Get back the reactive objects needed: 277 | my_maps=MY_maps() 278 | nb_de_carte=length(my_maps) 279 | map_files=MY_map_files() 280 | 281 | # Merge the n maps together: 282 | data=merge(my_maps[[1]] , my_maps[[2]], by.x=2 , by.y=2 , all=T) 283 | colnames(data)=c("marker",paste("chromo",map_files[1],sep="_") , paste("pos",map_files[1],sep="_") , paste("chromo",map_files[2],sep="_") , paste("pos",map_files[2],sep="_")) 284 | if(nb_de_carte>2){ 285 | for(i in c(3:nb_de_carte)){ 286 | data=merge(data , my_maps[[i]] , by.x=1 , by.y=2 , all=T) 287 | colnames(data)[c( ncol(data)-1 , ncol(data) )]= c( paste("chromo",map_files[i],sep="_") , paste("pos",map_files[i],sep="_") ) 288 | }} 289 | 290 | 291 | # I have now a file summarizing the information for every markers present at least one time ! Return it! 292 | return(data) 293 | }) 294 | 295 | # Check everything worked properly 296 | #observe({ print("summary du fichier mergé data:") ; print ( head( MY_data() ) ) }) 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | # 4/ --- List of chromosomes ? 305 | MY_chromosome_list=reactive({ 306 | 307 | # Get back the reactive objects needed: 308 | data=MY_data() 309 | 310 | # --- Get a list with the existing chromosomes: 311 | chromosome_list=unlist(data[ , seq(2,ncol(data),2) ]) 312 | chromosome_list=as.character(unique(sort( chromosome_list[!is.na(chromosome_list)] ))) 313 | 314 | # Return the chromosome liste 315 | return(chromosome_list) 316 | }) 317 | 318 | # Did it work ? 319 | #observe({ print("Liste des chromosomes:") ; print ( head( MY_chromosome_list() ) ) }) 320 | 321 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 331 | 332 | #----------------------------------------------------------------------------- 333 | # --- COMPUTE SUMMARY STATISTICS FOR EVERY MAPS 334 | #----------------------------------------------------------------------------- 335 | 336 | MY_summary_stat=reactive({ 337 | 338 | # Get the needed reactive objects: 339 | my_maps=MY_maps() 340 | nb_de_carte=length(my_maps) 341 | 342 | # Function 1 : give it a piece of map, it calculates some statistics and add it to a bilan data frame. 343 | my_fun=function(my_map, bilan, i){ 344 | num=nrow(bilan) 345 | num=num+1 346 | bilan[num,1]=i 347 | bilan[num,2]=nrow(my_map) 348 | bilan[num,3]=max(my_map[,3]) 349 | # Calcul des gaps: je vais prendre les gaps entre position unique, pas les gaps entre chaque marqueurs ! 350 | gaps = sort(my_map[,3])[-1] - sort(my_map[,3])[-length(my_map[,3])] 351 | gaps=gaps[gaps!=0] 352 | bilan[num,4]=round(mean(gaps),2) 353 | bilan[num,5]=max(gaps) 354 | bilan[num,6]=nrow(unique(my_map[,c(1,3)])) 355 | return(bilan) 356 | } 357 | 358 | # Compute summary statistics for every maps applying this function ! 359 | summary_stat=list() 360 | for(j in 1:nb_de_carte){ 361 | # Make an emty matrix 362 | map=my_maps[[j]] 363 | bilan=data.frame(matrix(0,0,6)) ; num=0 364 | colnames(bilan)=c("Chr.","#markers","map_size","average gap_size","biggest gap_size","#unique positions") 365 | # Apply the my_fun function to each chromosome one by one 366 | for(i in levels(map[,1])){ 367 | map_K=map[map[,1]==i,] 368 | bilan=my_fun(map_K , bilan , i) 369 | } 370 | # And then to the whole map 371 | i="tot" 372 | bilan=my_fun(map , bilan , "all") 373 | #Correct map size 374 | bilan[nrow(bilan) , 3] = sum(bilan[ -nrow(bilan) ,3]) 375 | #Correctaverage gap size 376 | bilan[nrow(bilan) , 4] = round(mean(bilan[ -nrow(bilan) ,4], na.rm=T),2) 377 | #Correct biggest gap 378 | bilan[nrow(bilan) , 5] = max(bilan[ -nrow(bilan) ,5], na.rm=T) 379 | #Add the result to the list containing all the map summaries 380 | summary_stat[[length(summary_stat)+1]]=bilan 381 | } 382 | 383 | # If I want the summary of the first map : summary_stat[[1]] 384 | return(summary_stat) 385 | 386 | }) 387 | 388 | # Check if everything is all right 389 | #observe({ print("fichier de summary statistique:") ; for(u in MY_summary_stat()) {print ( u )} }) 390 | 391 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 402 | 403 | # -------------------------------------------------------------------------------- 404 | # CREATION OF THE DYNAMICS BUTTONS FOR THE UI SCRIPT 405 | #-------------------------------------------------------------------------------- 406 | 407 | 408 | # ======== sheet2: Summary Statistics ========= 409 | # MAP to study 410 | output$choose_maps_sheet2<- renderUI({ checkboxGroupInput("selected_maps_sheet2", legend2[9], choices=MY_map_files(), selected=c(MY_map_files()[1],MY_map_files()[2]) , inline=T) }) 411 | # Chromosomes to study for markers density 412 | output$choose_chromo_sheet2<- renderUI({checkboxGroupInput( "chromo_sheet2", legend2[10], choices=MY_chromosome_list() , selected =c(MY_chromosome_list()[1],MY_chromosome_list()[2]) , inline = TRUE ) }) 413 | # Map to study for summary table 414 | output$choose_maps_sheet2_bis<- renderUI({ radioButtons("selected_maps_sheet2_bis", legend2[11], choices=MY_map_files(), selected=c(MY_map_files()[1]) , inline=T) }) 415 | 416 | 417 | # ======== sheet3: Compare Positions ========= 418 | # Map to study 419 | output$choose_maps3<- renderUI({ checkboxGroupInput("selected_maps", legend3[4], choices=MY_map_files(), selected=c(MY_map_files()[1],MY_map_files()[2]) , inline=T) }) 420 | # Chromosomes to study 421 | output$choose_chromo_sheet3<- renderUI({ radioButtons( "chromo", legend3[5], choices=MY_chromosome_list() , selected =MY_chromosome_list()[1] , inline=T ) }) 422 | 423 | 424 | # ======== sheet4: Interchromosomal Analyse ========= 425 | # First map to study : 426 | output$map1<- renderUI({ radioButtons("map1", legend4[4], choices=MY_map_files(), selected=MY_map_files()[1] ) }) 427 | # Second map to study : 428 | output$map2<- renderUI({ radioButtons("map2", legend4[5], choices=MY_map_files(), selected=MY_map_files()[2] ) }) 429 | # Chromosomes to study 430 | output$choose_chromo_sheet4<- renderUI({ selectInput( "chromo_sheet4", legend4[6], choices=c("all", MY_chromosome_list()) , selected =c("all") ) }) 431 | 432 | 433 | # ======== sheet5: Rough Map vizualisation ========= 434 | # MAP to study 435 | output$choose_maps5<- renderUI({ radioButtons("selected_maps_sheet5", legend5[3], choices=MY_map_files(), selected=MY_map_files()[1] ) }) 436 | # Chromosomes to study 437 | output$choose_chromo_sheet5<- renderUI({ checkboxGroupInput( "chromo_sheet5", legend5[4], choices=c("all", MY_chromosome_list()) , selected =c(MY_chromosome_list()[1],MY_chromosome_list()[2]) , inline = TRUE ) }) 438 | 439 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 450 | 451 | #----------------------------------------------------------------------------- 452 | # --- SHEET 2 : SUMMARY STATISTICS PAGE - BARPLOT ! 453 | #----------------------------------------------------------------------------- 454 | 455 | 456 | output$my_barplot=renderPlot({ 457 | 458 | # Get the needed reactive objects: 459 | summary_stat=MY_summary_stat() 460 | my_map_files=unlist(MY_map_files()) 461 | 462 | # Selected variable ? 463 | selected_var=which(c("# markers","map size","average gap size","biggest gap size","# unique positions")%in%input$var_for_barplot) 464 | 465 | # Selected Maps ? 466 | selected_maps=which(my_map_files%in%input$selected_maps_sheet2) 467 | nb_selected_maps=length(selected_maps) 468 | 469 | # Create a table which gives this selected_variable for every selected maps and every chromosomes. 470 | barplot_table=summary_stat[[selected_maps[1]]] [,c(1,selected_var+1)] 471 | for(i in selected_maps[-1]){ 472 | barplot_table=merge(barplot_table , summary_stat[[i]] [,c(1,selected_var+1)] , by.x=1 , by.y=1 , all=T) 473 | } 474 | rownames(barplot_table)=barplot_table[,1] 475 | barplot_table=barplot_table[-nrow(barplot_table) , ] 476 | barplot_table=t(as.matrix(barplot_table[,-1])) 477 | 478 | # Make the barplot ! 479 | par(mar=c(3,3,3,8)) 480 | barplot(barplot_table , beside=T , col=my_colors[1:length(selected_maps)]) 481 | #mtext(legend[23] , col="#3C3C3C" , line=-3 , at=ncol(barplot_table)*nb_selected_maps+8) 482 | 483 | 484 | #Close the render-barplot 485 | }) 486 | 487 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 488 | 489 | 490 | 491 | 492 | 493 | 494 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 495 | 496 | #----------------------------------------------------------------------------- 497 | # --- SHEET 2 : SUMMARY STATISTICS PAGE - DONUT-PLOT ! 498 | #----------------------------------------------------------------------------- 499 | 500 | output$my_pieplot=renderPlot({ 501 | 502 | # Get the needed reactive objects: 503 | summary_stat=MY_summary_stat() 504 | map_files=MY_map_files() 505 | 506 | # Avoid bug when loading 507 | if (is.null(input$var_for_barplot) | is.null(input$selected_maps_sheet2) ) {return(NULL)} 508 | 509 | # Selected variable ? 510 | all_var=c("# markers","map size","average gap size","biggest gap size","# unique positions") 511 | selected_var=which(all_var%in%input$var_for_barplot) 512 | 513 | # Selected Maps ? 514 | selected_maps=which(map_files%in%input$selected_maps_sheet2) 515 | nb_selected_maps=length(selected_maps) 516 | 517 | # Create a table which gives this selected_variable for every selected maps and every chromosomes. 518 | barplot_table=summary_stat[[selected_maps[1]]] [,c(1,selected_var+1)] 519 | for(i in selected_maps[-1]){ 520 | barplot_table=merge(barplot_table , summary_stat[[i]] [,c(1,selected_var+1)] , by.x=1 , by.y=1 , all=T) 521 | } 522 | rownames(barplot_table)=barplot_table[,1] 523 | barplot_table=barplot_table[nrow(barplot_table) , ] 524 | barplot_table=t(as.matrix(barplot_table[,-1])) 525 | 526 | # Make the donut-plot ! 527 | par(mar=c(3,3,3,10)) 528 | my_labels=paste(map_files[selected_maps],"\n",all_var[selected_var]," : ",barplot_table,sep="") 529 | doughnut(barplot_table, col=my_colors , border="white" , inner.radius=0.5, labels=my_labels ) 530 | 531 | 532 | #Close the render-barplot 533 | }) 534 | 535 | 536 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 546 | 547 | #----------------------------------------------------------------------------- 548 | # --- SHEET 2 : SUMMARY STATISTICS PAGE - SUMMARY TABLE OF SELECTED MAP 549 | #----------------------------------------------------------------------------- 550 | 551 | 552 | observe({ 553 | 554 | # Selected Map ? 555 | selected_map=which(MY_map_files()%in%input$selected_maps_sheet2_bis) 556 | 557 | # bug if no map (loading) 558 | if ( length(selected_map)==0 ) {return(NULL)} 559 | 560 | # Avoid bug when loading 561 | if (is.null(input$selected_maps_sheet2_bis) ) {return(NULL)} 562 | 563 | 564 | # Get the desired summary stat 565 | toprint=MY_summary_stat()[[selected_map]] 566 | 567 | output$sum_table <- DT::renderDataTable( 568 | DT::datatable( toprint , rownames = FALSE , options = list(pageLength = 40, dom = 't' )) 569 | ) 570 | 571 | 572 | # Close observer 573 | }) 574 | 575 | 576 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 577 | 578 | 579 | 580 | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 589 | 590 | #----------------------------------------------------------------------------- 591 | # --- SHEET 2 : SUMMARY STATISTICS PAGE - PLOT FOR DENSITY ! 592 | #----------------------------------------------------------------------------- 593 | 594 | # Make the circular plot. See https://cran.r-project.org/web/packages/circlize/vignettes/circlize.pdf to understand how circular plot works. 595 | output$circular_plot <- renderPlot({ 596 | 597 | # Get the needed reactive objects: 598 | summary_stat=MY_summary_stat() 599 | map_files=MY_map_files() 600 | my_maps=MY_maps() 601 | nb_de_carte=length(map_files) 602 | 603 | # Avoid bug when loading 604 | if (is.null(input$var_for_barplot) | is.null(input$selected_maps_sheet2) ) {return(NULL)} 605 | 606 | # Which maps have been selected ? 607 | selected_maps=which(map_files%in%input$selected_maps_sheet2) 608 | nb_selected_maps=length(selected_maps) 609 | 610 | # Fichier nécessaire 611 | data_circ=data.frame() 612 | for(i in selected_maps){ 613 | current_map=my_maps[[i]] 614 | current_map$map_name=map_files[i] 615 | current_map$group_and_name=paste(map_files[i] , current_map[,1] , sep="_") 616 | data_circ=rbind(data_circ , current_map) 617 | } 618 | 619 | 620 | # If the "all" option is not selected, then I keep only the chosen chromosomes 621 | if(!("all"%in%input$chromo_sheet2)){ 622 | take=which(data_circ[,1]%in%input$chromo_sheet2) 623 | data_circ=data_circ[take , ] 624 | data_circ[,1]=droplevels(data_circ[,1]) 625 | } 626 | 627 | 628 | # Réalisation du graph 629 | par(mfrow=c(nb_de_carte ,1) , mar=c(0.3,4,0,0) ) 630 | for( map in levels(as.factor(data_circ$map_name))){ 631 | 632 | # Reset x positions 633 | vecX=c() 634 | vecY=c() 635 | vecSep=c(0) 636 | my_data=data_circ[which(data_circ$map_name==map) , ] 637 | 638 | for( chromo in levels(as.factor(my_data$group)) ){ 639 | 640 | don=my_data[which(my_data$group==chromo) , ] 641 | a=density(don$position) 642 | a$x=a$x + abs(min(a$x)) 643 | if(length(vecX)>0){a$x=a$x+max(vecX) } 644 | vecX=c(vecX , a$x) 645 | vecY=c(vecY , a$y) 646 | vecSep=c(vecSep, max(vecX)) 647 | } 648 | 649 | # print the plot 650 | plot(1,1,col="transparent" , xlim=c(0,max(vecX)) , ylim=c(0,max(vecY)) , xlab="" , xaxt="n" , ylab="" , yaxt="n" , bty="n" ) 651 | rect( vecSep[-length( vecSep)], rep(-2,length( vecSep)) , vecSep[-1] , rep(1 , length( vecSep)) , col=my_colors , border=F ) 652 | lines( vecX , vecY , col="black" , lwd=3 ) 653 | mtext( map , at=max(vecY)/2 , col="orange" , cex=2 , line=0, side=2 ) 654 | 655 | #fin du plot 656 | } 657 | 658 | #Ajout des labels de l'axe des x? 659 | mtext( levels(as.factor(my_data$group)) , at=(vecSep[-1]+vecSep[-length(vecSep)]) /2 , col="orange" , cex=2 , line=5, side=1 ) 660 | 661 | }) 662 | 663 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 664 | 665 | 666 | 667 | 668 | 669 | 670 | 671 | 672 | 673 | 674 | 675 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 676 | 677 | #----------------------------------------------------------------------------- 678 | # --- SHEET 3 : MAP COMPARISON FOR A CHOSEN CHROMOSOME 679 | #----------------------------------------------------------------------------- 680 | 681 | 682 | 683 | # define session specific variable 684 | # visible from all functions but session/user specific 685 | my_global_old_choice<-c(); 686 | liste_of_map_to_compare<-c(); 687 | 688 | 689 | # liste_of_map_to_compare is an object with the genetic maps to compare, in the good order. I initialize it with the 2 first maps, like in the radiobutton. 690 | MY_liste_of_map_to_compare=reactive({ 691 | map_files=MY_map_files() 692 | liste_of_map_to_compare=c(map_files[1],map_files[2]) 693 | return(liste_of_map_to_compare) 694 | }) 695 | 696 | 697 | 698 | output$plot1 <- renderPlotly({ 699 | 700 | # ========== PART 0 : INITIALISE OBJECTS 701 | #Get the needed reactive objects: 702 | summary_stat=MY_summary_stat() 703 | map_files=MY_map_files() 704 | my_maps=MY_maps() 705 | nb_de_carte=length(map_files) 706 | data=MY_data() 707 | 708 | # We need to remember the old choice 709 | old_choice <- my_global_old_choice; 710 | 711 | # I get the current choice of maps to show (this is not ordered, we have to order it): 712 | current_choice=input$selected_maps 713 | 714 | 715 | 716 | # ========== PART 1 : DETERMIN WHICH MAP HAS BEEN ADDED / REMOVED + CREATE THE ORDERED SELECTED MAP LIST 717 | # If the user has added a map, I determine which, and add it to the map to compare: 718 | if(is.null(old_choice) | is.null(current_choice) ){ 719 | liste_of_map_to_compare=current_choice 720 | }else{ 721 | intersection=which(old_choice%in%current_choice) 722 | if( length(intersection)==0 ){ 723 | to_del=old_choice 724 | }else{ 725 | to_del=old_choice[-intersection] 726 | } 727 | 728 | if(length(to_del)>0) 729 | {liste_of_map_to_compare=old_choice[ - which(old_choice%in%to_del) ]} 730 | 731 | intersection=which(current_choice%in%old_choice) 732 | if( length(intersection)==0 ){ 733 | to_add= current_choice 734 | }else{ 735 | to_add= current_choice[-intersection] 736 | } 737 | 738 | if(length(to_add)>0) 739 | liste_of_map_to_compare=c(liste_of_map_to_compare,to_add) 740 | } 741 | # I save the current choice as old_choice for next change: 742 | my_global_old_choice<<-liste_of_map_to_compare 743 | liste_of_map_to_compare<<-liste_of_map_to_compare 744 | 745 | 746 | 747 | 748 | 749 | 750 | # ========== PART 2 : IF NO SELECTED MAP, I RETURN A MESSAGE! 751 | validate( 752 | need(length(liste_of_map_to_compare)!=0, "Please select at least one map!") 753 | ) 754 | 755 | 756 | 757 | 758 | 759 | 760 | # ========== PART 3 : CREATE A TABLE WITH MAPS FEATURES, IN THE GOOD ORDER. ex:for mapB and mapA i keep column: 1,4,5, 2,3: 761 | # --- Make an input table with columns in the corresponding order: 762 | selected_maps=match(liste_of_map_to_compare , map_files) 763 | selected_col=rep(selected_maps , each=2)*2 764 | selected_col=c(1,selected_col+rep(c(0,1) , length(selected_col)/2)) 765 | dat=data[ , selected_col ] 766 | nb_selected_maps=length(selected_maps) 767 | 768 | # --- Subset of the dataset with only the good chromosome 769 | my_fun=function(x){ a=length(which(x==input$chromo)) ; return( a ) } 770 | nb_good_chromo=apply(dat , 1 , my_fun) 771 | don=dat[ which(nb_good_chromo>0 ) , ] 772 | # Put NA when a marker is attributed to another chromosome 773 | if(length(selected_maps)>1){for(i in seq(2,100,2)[1:nb_selected_maps] ){ 774 | tmp=don[,i] 775 | tmp=which( tmp!=input$chromo ) 776 | don[tmp, c(i,i+1)]=NA 777 | }} 778 | 779 | # --- If the "normalize box" is choosen, I normalize length 780 | if(input$ask_for_normalize==TRUE){ 781 | for(i in seq(3,ncol(don),2)){ 782 | don[,i]=don[,i]*100/max(don[,i],na.rm=T) 783 | }} 784 | 785 | 786 | # ========== PART 4 : IF ONE MAP IS SELECTED ONLY, I GIVE A CERTAIN TYPE OF PLOT 787 | if(nb_selected_maps<2){ 788 | return( 789 | plot_ly(x=rep(1,nrow(don)),y=don[,3], type="scatter", mode="marker", hoverinfo="text", text=paste(don[,1], "
", "position: ",don[,3],sep="") , marker=list(size=10) )%>% 790 | layout(hovermode="closest", 791 | xaxis=list(title = "", zeroline = FALSE, showline = FALSE, showticklabels = FALSE, showgrid = FALSE , range=c(0.5,1.5) ), 792 | yaxis=list( autorange = "reversed", title = "Position (cM)", zeroline = F, showline = T, showticklabels = T, showgrid = FALSE , tickfont=list(color="grey", size=15) , titlefont=list(color="grey", size=15) , tickcolor="grey" , linecolor="grey") 793 | )) 794 | } 795 | 796 | 797 | 798 | 799 | 800 | # ========== PART 5 : COMPARISON PLOT IF I HAVE AT LEAST 2 MAPS SELECTED 801 | 802 | # --- PART 5.1: CREATE THE Y AXIS OF LINK BETWEEN MAPS 803 | # Je fais une fonction qui me fait 2 vecteurs de positions pour 2 cartes données : AXE des Y 804 | # There is one vector for the problematic vectors, and one for the not problematic markers. 805 | function_pos=function(x,y){ 806 | 807 | #Récupération de 2 carte seulement: 808 | pos=na.omit(don[,c(1,x,y)]) 809 | 810 | # Find the non-problematic markers and count them 811 | M1=pos$marker[order(pos[,2], pos[,3])] 812 | M2=pos$marker[order(pos[,3], pos[,2])] 813 | my_notprobmarkers=LCS(as.character(M1),as.character(M2))$LCS 814 | pos_not_prob=pos[which(pos$marker%in%my_notprobmarkers) , ] 815 | pos_prob=pos[-which(pos$marker%in%my_notprobmarkers) , ] 816 | nb_not_prob=nrow(pos_not_prob) 817 | nb_prob=nrow(pos_prob) 818 | 819 | #Il faut que je fasse 2 vecteurs avec les valeur en cM dans l'ordre 820 | 821 | # --> NOT PROBLEMATIC MARKERS 822 | my_vect=as.vector(t(as.matrix(pos_not_prob[,c(2,3)]))) 823 | correctif=seq(1:length(my_vect)) + rep(c(0,0,1,-1) , length.out=length(my_vect) ) 824 | my_vect=my_vect[correctif] 825 | #Mais attention probleme! si je fini sur la carte de gauche, il faut que je revienne a la carte de droite avant de passer a la paire de carte suivante! 826 | if(length(my_vect)%%4 == 0){ my_vect=c(my_vect , my_vect[length(my_vect)] , my_vect[length(my_vect)-1]) } 827 | my_vect_not_prob=my_vect 828 | 829 | # --> PROBLEMATIC MARKERS 830 | my_vect=as.vector(t(as.matrix(pos_prob[,c(2,3)]))) 831 | correctif=seq(1:length(my_vect)) + rep(c(0,0,1,-1) , length.out=length(my_vect) ) 832 | my_vect=my_vect[correctif] 833 | #Mais attention probleme! si je fini sur la carte de gauche, il faut que je revienne a la carte de droite avant de passer a la paire de carte suivante! 834 | if(length(my_vect)%%4 == 0){ my_vect=c(my_vect , my_vect[length(my_vect)] , my_vect[length(my_vect)-1]) } 835 | my_vect_prob=my_vect 836 | 837 | # Return the non problematic and problematic marker vectors 838 | return( list(my_vect_not_prob, my_vect_prob, nb_not_prob, nb_prob) ) 839 | } 840 | 841 | # Apply the function to the selected maps. 842 | pos_final_not_prob=c() 843 | pos_final_prob=c() 844 | nb_of_not_prob=c() 845 | nb_of_prob=c() 846 | for(v in c(1:(nb_selected_maps-1))){ 847 | col_x=v*2+1 848 | col_y=v*2+3 849 | a=function_pos( col_x , col_y) 850 | pos_final_not_prob=c(pos_final_not_prob,a[[1]]) 851 | pos_final_prob=c(pos_final_prob,a[[2]]) 852 | nb_of_not_prob=c(nb_of_not_prob,a[[3]]) 853 | nb_of_prob=c(nb_of_prob,a[[4]]) 854 | } 855 | 856 | 857 | # --- PART 5.2: CREATE THE X AXIS OF LINK BETWEEN MAPS 858 | # Once more I do 2 vectors: one for the good markers, one for the problematic ones 859 | xaxis_not_prob=c() 860 | xaxis_prob=c() 861 | num=0 862 | for(i in c(1:(nb_selected_maps-1))){ 863 | num=num+1 864 | 865 | # not problematic markers 866 | my_nb=nb_of_not_prob[num] 867 | if(my_nb==1){ to_add=c(num,num+1) }else{ to_add=rep(c(num,num+1,num+1,num),my_nb/2) } 868 | if(length(to_add)%%4 == 0){ to_add=c(to_add , to_add[length(to_add)] , to_add[length(to_add)-1]) } 869 | xaxis_not_prob=c(xaxis_not_prob,to_add) 870 | 871 | # problematic markers 872 | my_nb=nb_of_prob[num] 873 | if(my_nb==1){ to_add=c(num,num+1) }else{ to_add=rep(c(num,num+1,num+1,num),my_nb/2) } 874 | if(length(to_add)%%4 == 0){ to_add=c(to_add , to_add[length(to_add)] , to_add[length(to_add)-1]) } 875 | xaxis_prob=c(xaxis_prob,to_add) 876 | 877 | } 878 | 879 | 880 | # --- PART 5.3: MAKE THE GRAPH WITH PLOTLY 881 | p=plot_ly()%>% 882 | add_trace(x=xaxis_not_prob , y=pos_final_not_prob, hoverinfo="none" , type="scatter", mode="lines", line=list(width=input$thickness, color=input$my_color , opacity=0.1), showlegend=FALSE )%>% 883 | 884 | # Add problematic markers 885 | add_trace(x=xaxis_prob , y=pos_final_prob , hoverinfo="none" , type="scatter", mode="lines", line=list(width=input$thickness, color=input$my_color_bad , opacity=0.1) , showlegend=F)%>% 886 | 887 | # Custom the layout 888 | layout( 889 | #Gestion du hovermode 890 | hovermode="closest" , 891 | # Gestion des axes 892 | xaxis=list(title = "", zeroline = FALSE, showline = FALSE, showticklabels = FALSE, showgrid = FALSE , range=c(0.5,nb_selected_maps+0.5) ), 893 | yaxis=list(range=c(0,500), autorange = "reversed", title = "Position (cM)", zeroline = F, showline = T, showticklabels = T, showgrid = FALSE , tickfont=list(color="grey", size=15) , titlefont=list(color="grey", size=15) , tickcolor="grey" , linecolor="grey") 894 | ) 895 | 896 | # Add vertical lines to represent chromosomes. 897 | for(m in c(1:nb_selected_maps)){ 898 | p=add_trace(p, x=c(m,m), y=c(0, max(don[,m*2+1],na.rm=T)) , type="scatter", mode="lines" , line=list(width=7, color="black", opacity=1),showlegend=F )%>% 899 | layout( yaxis=list(range=c(0,max(don[,m*2+1],na.rm=T))) ) 900 | } 901 | 902 | # Add markers 903 | for(m in c(1:nb_selected_maps)){ 904 | obj2=don[,c(1,m*2+1)] 905 | obj2$text=paste(obj2[,1],"\npos: ",obj2[,2],sep="") 906 | p=add_trace(p, x=rep(m,nrow(obj2) ) , y=obj2[,2] , type="scatter", mode="markers+lines", line=list(color="black", width=8), marker=list(color="black" , size=12 , opacity=0.5,symbol=24) , text=obj2$text , hoverinfo="text", showlegend=F)%>% 907 | layout( yaxis=list(range=c(0,max(pos_final_not_prob))) ) 908 | } 909 | 910 | # Add maps names 911 | p=add_trace(p, x=seq(1:nb_selected_maps) , y=rep(-10,nb_selected_maps) , text=unlist(liste_of_map_to_compare) , type="scatter" , mode="lines+text" , textfont=list(size=20 , color=input$my_color_name), line=list(color="transparent"), showlegend=F ) 912 | 913 | 914 | 915 | # --- PART 5.4: IF THE USER NEED THE PROBLEMATIC MARKERS 916 | list_prob_print=c() 917 | for(i in seq(3,(ncol(don)-2),2 ) ){ 918 | for(v in seq(5,ncol(don),2)){ 919 | if(v>i){ 920 | pos=na.omit(don[,c(1,i,v)]) 921 | M1=pos$marker[order(pos[,2], pos[,3])] 922 | M2=pos$marker[order(pos[,3], pos[,2])] 923 | my_notprobmarkers=LCS(as.character(M1),as.character(M2))$LCS 924 | my_probmarkers=pos$marker[-which(pos$marker%in%my_notprobmarkers)] 925 | list_prob_print=c(list_prob_print, as.character(my_probmarkers)) 926 | }}} 927 | list_prob_print=data.frame(problematic_markers=unique(list_prob_print)) 928 | output$downloadID <- downloadHandler( 929 | filename = function() { paste('Prob_markers_GenMapComp', Sys.Date(), '.csv', sep='') }, 930 | content = function(file) { write.table(list_prob_print, file, row.names=FALSE)} 931 | ) 932 | 933 | #print plotly graph 934 | p 935 | }) 936 | 937 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 938 | 939 | 940 | 941 | 942 | 943 | 944 | 945 | 946 | 947 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 948 | 949 | #----------------------------------------------------------------------------- 950 | # --- SHEET 4 : INTER CHROMOSOME ANALYSIS 951 | #----------------------------------------------------------------------------- 952 | 953 | 954 | output$plot2 <- renderPlotly({ 955 | 956 | # Get the needed reactive objects: 957 | summary_stat=MY_summary_stat() 958 | map_files=MY_map_files() 959 | my_maps=MY_maps() 960 | nb_de_carte=length(map_files) 961 | 962 | # Avoid bug when loading 963 | if (is.null(input$map1) | is.null(input$map2) | is.null(input$chromo_sheet4) ) {return(NULL)} 964 | 965 | # Get the first selected map 966 | selected=which(map_files%in%input$map1) 967 | map1=my_maps[[selected]] 968 | name1=map_files[selected] 969 | 970 | # Get the second selected map 971 | selected=which(map_files%in%input$map2) 972 | map2=my_maps[[selected]] 973 | name2=map_files[selected] 974 | 975 | # Select the choosen chromosome, the user can choose "all" ! 976 | if(input$chromo_sheet4=="all"){map1=map1}else{map1=map1[map1[,1]==input$chromo_sheet4 , ] ; map2=map2[map2[,1]==input$chromo_sheet4 , ]} 977 | 978 | 979 | 980 | # a little function: I remake the x axis to add chromosomes beside each others. 981 | my_fun=function(a){ 982 | last=0 983 | to_add=0 984 | out=c() 985 | for (i in a){ 986 | if(i>=last) { sortie=i+to_add } 987 | if(i10000, -100 , -0.05*max(map1max[,2]) ) 1023 | 1024 | # Prepare rectangles 1025 | my_list_rect=list() 1026 | if(input$chromo_sheet4=="all"){ 1027 | for(i in c(1:nrow(map1max))){ 1028 | L1=list(type = "rect", fillcolor=input$col_s4_1 , line = list(color=input$col_s4_1), opacity = 0.2, x0 = map1min[i,2], x1 = map1max[i,2], xref = "x", y0 = map2min[i,2], y1 = map2max[i,2], yref = "y" ) 1029 | my_list_rect[[ length(my_list_rect)+1]] = L1 1030 | }} 1031 | 1032 | # Prepare lines to make a grid 1033 | my_list_lines=list() 1034 | if(input$chromo_sheet4=="all"){ 1035 | for (i in map1max[,2]) { 1036 | L1 <- list( type="line", line=list(color = "grey", width=0.4), xref="x", yref="y", x0=i, x1=i, y0=0, y1=max(map2max[,2],na.rm=T) ) 1037 | my_list_lines[[ length(my_list_lines)+1]] = L1 1038 | } 1039 | for (i in map2max[,2]) { 1040 | L1 <- list( type="line", line=list(color = "grey", width=0.4), xref="x", yref="y", x0=0, x1=max(map1max[,2],na.rm=T), y0=i, y1=i ) 1041 | my_list_lines[[ length(my_list_lines)+1]] = L1 1042 | }} 1043 | 1044 | 1045 | # --- Make the plot ! 1046 | p <- plot_ly() %>% 1047 | 1048 | # markers 1049 | add_trace( x=don[,4] , y=don[,7] , type="scatter", mode="markers" , text=don$text , hoverinfo="text" , marker=list( size=15 , opacity=0.5, color=ifelse( as.character(don$group.x)==as.character(don$group.y) , input$col_s4_2 , input$col_s4_3)), showlegend=F ) %>% 1050 | 1051 | # Layout 1052 | layout( 1053 | hovermode="closest" , 1054 | xaxis=lay_x , 1055 | yaxis=lay_y 1056 | ) %>% 1057 | 1058 | # Add rectangles and lines 1059 | layout( title = "", 1060 | shapes = c(my_list_rect, my_list_lines) 1061 | ) 1062 | 1063 | #plot it! 1064 | p 1065 | 1066 | 1067 | #Je ferme le outputPlot2 1068 | }) 1069 | 1070 | 1071 | output$key_numbers_sheet_3 <- renderPlot({ 1072 | 1073 | # Get the needed reactive objects: 1074 | summary_stat=MY_summary_stat() 1075 | map_files=MY_map_files() 1076 | my_maps=MY_maps() 1077 | nb_de_carte=length(map_files) 1078 | 1079 | # Avoid bug when loading 1080 | if (is.null(input$map1) | is.null(input$map2) | is.null(input$chromo_sheet4) ) {return(NULL)} 1081 | 1082 | # Get the first selected map 1083 | selected=which(map_files%in%input$map1) 1084 | map1=my_maps[[selected]] 1085 | name1=map_files[selected] 1086 | 1087 | # Get the second selected map 1088 | selected=which(map_files%in%input$map2) 1089 | map2=my_maps[[selected]] 1090 | name2=map_files[selected] 1091 | 1092 | # Select the choosen chromosome, the user can choose "all" ! 1093 | if(input$chromo_sheet4=="all"){map1=map1}else{map1=map1[map1[,1]==input$chromo_sheet4 , ] ; map2=map2[map2[,1]==input$chromo_sheet4 , ]} 1094 | 1095 | # Compute basic statistics: 1096 | nb_mark_map1=nrow(map1) 1097 | nb_mark_map2=nrow(map2) 1098 | nb_common_mark=length(which(map1$marker%in%map2$marker)) 1099 | tmp=merge(map1,map2,by.x=2,by.y=2)[c(3,5)] 1100 | coeff_cor=round(cor(tmp[,1] , tmp[,2] , method="spearman"),2) 1101 | 1102 | # Then I make the "plot" 1103 | par(bg="transparent" , mar=c(0,0,0,0) ) 1104 | plot(1,1,xaxt="n", yaxt="n",bty="n",xlab="",ylab="", col="transparent", xlim=c(0,4) , ylim=c(0.5,4.5) ) 1105 | text(rep(1 ,4) , c(4,3,2,1) , c(nb_mark_map1, nb_mark_map2, nb_common_mark, coeff_cor), col="orange" , cex=3 , adj=1 , font=2 ) 1106 | text(rep(1.2,4) , c(4,3,2,1) , c(paste("markers in\n",name1, " map",sep=""), paste("markers in\n",name2," map",sep=""), "common\nmarkers","Spearman\ncorrelation") , col="grey" , cex=1.3 , font=2 , adj=0 ) 1107 | 1108 | #Je ferme le output avec un fond transparent 1109 | }, bg="transparent") 1110 | 1111 | 1112 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1113 | 1114 | 1115 | 1116 | 1117 | 1118 | 1119 | 1120 | 1121 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1122 | 1123 | #----------------------------------------------------------------------------- 1124 | # --- SHEET 5 : RAW MAP VIZUALIZATION 1125 | #----------------------------------------------------------------------------- 1126 | 1127 | 1128 | #Faire une réactive pour pouvoir faire le tableau désiré 1129 | observe({ 1130 | 1131 | # Get the needed reactive objects: 1132 | map_files=MY_map_files() 1133 | my_maps=MY_maps() 1134 | 1135 | # Avoid bug when loading 1136 | if (is.null(input$selected_maps_sheet5) ) {return(NULL)} 1137 | 1138 | #Get the selected map 1139 | selected=which(map_files%in%input$selected_maps_sheet5) 1140 | if(length(selected==1)){ 1141 | 1142 | #Get the selected map 1143 | data_for_map_table=my_maps[[selected]] 1144 | 1145 | #Get the selected chromosomes 1146 | if(!("all"%in%input$chromo_sheet5)){ 1147 | selected_chromosomes=input$chromo_sheet5 1148 | data_for_map_table=data_for_map_table[which(data_for_map_table[,1]%in%selected_chromosomes),] 1149 | } 1150 | 1151 | #Close the if statement 1152 | } 1153 | 1154 | #Make the graph 1155 | output$my_rough_map_viz <- renderDataTable( 1156 | 1157 | #See https://rstudio.github.io/DT/options.html for options in printing table 1158 | data_for_map_table , escape = F , rownames = FALSE , options = list(pageLength = 40, bFilter=F) 1159 | 1160 | #Close the output 1161 | ) 1162 | 1163 | #Close the observe 1164 | }) 1165 | 1166 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1167 | 1168 | 1169 | 1170 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1171 | 1172 | # Just 3 small tables for the documentation page 1173 | a=c("marker_52", "marker_23", "marker_18", "marker_8", "marker_12", "marker_3", "marker_98", "marker_72") 1174 | b=c(0.0,29.4,31.2,40.5,0.0,3.3,4.6,10.8) 1175 | c=c(rep("LG1",4),rep("LG2",4)) 1176 | ex1=data.frame(LG=c , marker=a, position=b) 1177 | output$doc_ex1 <- DT::renderDataTable( 1178 | DT::datatable(ex1 , rownames = FALSE , options = list(dom = 't' )) 1179 | ) 1180 | 1181 | # Just 3 small tables for the documentation page 1182 | a=c("group", "marker_52", "marker_23", "marker_18", "marker_8", "group", "marker_12", "marker_3", "marker_98", "marker_72") 1183 | b=c("LG1",0.0,29.4,31.2,40.5,"LG2",0.0,3.3,4.6,10.8) 1184 | ex2=data.frame(a , b) 1185 | output$doc_ex2 <- DT::renderDataTable( 1186 | DT::datatable(ex2 , rownames = FALSE , colnames="", options = list(dom = 't' )) 1187 | ) 1188 | 1189 | # Just 3 small tables for the documentation page 1190 | ex3=data.frame("0 -801.56 {1 -485.24 MS4 0.0 MS5 3.3 MS13 38.8 MS6 64.2 MS11 86.4 MS17 137.9 MS16 159.5 MS8 186.5 MS7 192.4 MS2 207.4 MS3 208.0 MS9 231.5 MS15 249.3 MS12 252.8 MS20 291.1 MS19 293.8 MS1 483.9} {2 -316.32 MS4 0.0 MS5 84.4 MS6 138.5 MS8 229.0 MS7 307.2 MS3 493.5 MS9 706.1 MS15 862.8 MS1 1622.9 G36 1726.5 G39 1786.2 G37 1845.9 G40 1871.3}") 1191 | output$doc_ex3 <- DT::renderDataTable( 1192 | DT::datatable(ex3 , rownames = FALSE , colnames="", options = list(dom = 't' )) 1193 | ) 1194 | 1195 | 1196 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1197 | 1198 | 1199 | 1200 | 1201 | #Je ferme le shinyServer 1202 | }) 1203 | 1204 | 1205 | 1206 | 1207 | 1208 | 1209 | 1210 | 1211 | 1212 | #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------# 1213 | 1214 | -------------------------------------------------------------------------------- /ui.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | ################################################ 4 | # 5 | # THE GENETIC MAP COMPARATOR 6 | # 7 | ############################################### 8 | 9 | 10 | shinyUI(navbarPage( 11 | 12 | "The Genetic Map Comparator", 13 | 14 | # Choose a theme ! 15 | theme = shinytheme("united"), 16 | 17 | # And I custom it with additionnal CSS 18 | header=includeCSS("www/genComp.css") , 19 | 20 | 21 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 22 | 23 | 24 | 25 | 26 | 27 | 28 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 29 | 30 | # ---------------------- 31 | # SHEET 1 : HOME PAGE 32 | # ---------------------- 33 | 34 | tabPanel( 35 | 36 | # Name 37 | h4(legend1[1]) , 38 | 39 | # Only one zone for the home page 40 | column(12, offset=0, align="center" , 41 | 42 | # Set the style of this page 43 | style=" 44 | background-image: url(my_image.png); 45 | opacity: 1; 46 | background-color: black; 47 | margin-top: -20px; 48 | width: 100%; 49 | color: white; 50 | font-size:14pt; 51 | 52 | ", 53 | 54 | # And write the welcome message 55 | br(""), 56 | #helpText(strong(legend1[2] , style="color:white ; font-family: 'times'; font-size:50pt ; font-type:bold" ) ) , 57 | #br(""), 58 | helpText(strong(p(legend1[3] , style="color:white ; font-family: 'times'; font-size:18pt"))) , 59 | #br(""), 60 | 61 | # widget to choose several files: 62 | fileInput("file1", strong(p(legend1[4] , style="color:orange ; font-family: 'times'; font-size:18pt")) , multiple = TRUE, accept=NULL), 63 | uiOutput("error_message"), 64 | 65 | # widget to propose 3 exemples 66 | radioButtons("file2", strong(p(legend1[5] , style="color:orange ; font-family: 'times'; font-size:18pt")), choices = c("sorghum (Mace et al. 2009)","wheat (Maccaferri et al. 2015)", "wheat (Holtz et al. 2016)"), selected =c("sorghum (Mace et al. 2009)") , inline = FALSE ), 67 | 68 | #br(), 69 | helpText(strong(p(legend1[6] , style="color:orange ; font-family: 'times'; font-size:18pt"))) , 70 | #legend1[7], 71 | 72 | # Last part with our names. Not in the legend file.. 73 | br(""),br(""),br(""), 74 | p( 75 | legend1[8], 76 | a(em("Yan Holtz") , style="color:white ; font-family:'times'; font-size:15pt", href = "https://holtzyan.wordpress.com/" , target="_blank"), 77 | " & ", 78 | a(em("Vincent Ranwez") , style="color:white ; font-family:'times'; font-size:15pt", href = "https://sites.google.com/site/ranwez/" , target="_blank"), 79 | " & ", 80 | a(em("Jacques David") , style="color:white ; font-family:'times'; font-size:15pt", href = "https://www.researchgate.net/profile/Jacques_David4" , target="_blank"), 81 | style="color:white ; font-family: 'times'; font-size:15pt" 82 | ), 83 | 84 | br(""),br(""),br(""),br(""),br(""),br(""),br(""),br(""),br(""),br("") 85 | 86 | 87 | #Close column 88 | ) 89 | 90 | #Close the tabPanel 91 | ), 92 | 93 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 106 | 107 | # ---------------------- 108 | # SHEET 2 : SUMMARY STATISTICS 109 | # ---------------------- 110 | tabPanel( 111 | 112 | #Name 113 | h4(legend2[1]), 114 | 115 | # ==== Title 2 in Orange 116 | fluidRow(align="center", 117 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 118 | column(6,offset=3, 119 | br(), 120 | helpText( strong(legend2[5] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 121 | hr() 122 | )), 123 | 124 | # === Some text to explain the Figure: 125 | fluidRow(align="justify", 126 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 127 | column(6,offset=3, 128 | br(),legend2[6],br() 129 | ) 130 | ),br(),br(), 131 | 132 | # === One widget to select maps and variables for pie and barplot: 133 | fluidRow( align="center", 134 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 135 | column(6,offset=3, 136 | wellPanel(uiOutput("choose_maps_sheet2_bis")) 137 | ) 138 | ), 139 | 140 | # === Fluid row for the summary table 141 | br(""), 142 | fluidRow(align="center", 143 | column(12, offset=0, 144 | dataTableOutput('sum_table' , width="700px") 145 | ) 146 | ),br(),br(), 147 | 148 | 149 | # ==== Title in Orange 150 | fluidRow(align="center", 151 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 152 | column(6,offset=3, 153 | br(), 154 | helpText( strong(legend2[2] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 155 | hr() 156 | )), 157 | 158 | 159 | # === Some text to explain the Figure: 160 | fluidRow(align="justify", 161 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 162 | column(6,offset=3, 163 | br(),legend2[3],br(),br(),legend2[4],br(),br(),legend2[12] 164 | ) 165 | ),br(),br(), 166 | 167 | # === Two widgets to select maps and variables for pie and barplot: 168 | fluidRow( 169 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 170 | column(3,offset=3, 171 | wellPanel(uiOutput("choose_maps_sheet2")) 172 | ), 173 | column(3,offset=0, 174 | #wellPanel(radioButtons( "var_for_barplot", legend2[41], choices = c("nb. marker","size","average gap","biggest gap","Nb. uniq pos."), selected =c("nb. marker") , inline = FALSE )) 175 | wellPanel(radioButtons( "var_for_barplot", legend2[13], choices = c("# markers","map size","average gap size","biggest gap size","# unique positions"), selected =c("# markers") , inline = FALSE )) 176 | 177 | ) 178 | ), 179 | 180 | 181 | # === Bar and pieplot whit corresponding widget 182 | fluidRow( 183 | 184 | # PiePlot 185 | column(3, offset=1, plotOutput("my_pieplot", height = "500px" , width = "500px" ) ), 186 | 187 | #Barplot 188 | column(7 , plotOutput("my_barplot" , height = "500px" , width = "1000px") ) 189 | 190 | ),br(), 191 | 192 | # === Separation 193 | #fluidRow( column( 6,offset=3, hr())), 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | # ==== Title 3 in Orange 204 | fluidRow(align="center", 205 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 206 | column(6,offset=3, 207 | br(), 208 | helpText( strong(legend2[7] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 209 | hr() 210 | )), 211 | 212 | 213 | # === Some text to explain the Figure: 214 | fluidRow(align="justify", 215 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 216 | column(6,offset=3, 217 | br(),legend2[8],br() 218 | ) 219 | ),br(),br(), 220 | 221 | # === One widget to select maps and variables for pie and barplot: 222 | fluidRow( align="center", 223 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 224 | column(6,offset=3, 225 | wellPanel(uiOutput("choose_chromo_sheet2")) 226 | ) 227 | ), 228 | 229 | # === Fluid row for the density plot ! 230 | br(""), 231 | fluidRow(align="center", 232 | column(12, offset=0, 233 | plotOutput("circular_plot" , height = "1200px" , width = "900px" ) 234 | ) 235 | ),br(),br() 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | #Close the tabPanel 246 | ), 247 | 248 | 249 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 259 | 260 | # ---------------------- 261 | # SHEET 3 : COMPARISON OF MAPS 262 | # ---------------------- 263 | 264 | tabPanel( class = "two", 265 | 266 | #Name 267 | h4(legend3[1]) , 268 | 269 | # ==== Title in Orange 270 | fluidRow(align="center", 271 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 272 | column(6,offset=3, 273 | br(), 274 | helpText( strong(legend3[2] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 275 | hr() 276 | )), 277 | 278 | 279 | # === Some text to explain the Figure: 280 | fluidRow(align="justify", 281 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 282 | column(6,offset=3, 283 | br(),legend3[3],legend3[6],br(),br(),legend3[7],a(em("colour.") , style="color:blue", href = "http://www.color-hex.com/" , target="_blank"), 284 | br(),br(),legend3[8],br() 285 | )),br(),br(), 286 | 287 | 288 | # === 2 first widgets 289 | fluidRow( align="center", 290 | column(6,offset=3, 291 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 292 | column(3,offset=0, wellPanel(uiOutput("choose_chromo_sheet3"))), 293 | column(3,offset=0, wellPanel(uiOutput("choose_maps3"))) 294 | )), 295 | 296 | # === 2 Next widgets 297 | fluidRow( align="center", 298 | column(6,offset=3, 299 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 300 | column(3,offset=0, wellPanel(checkboxInput("ask_for_normalize", "normalize map lenghts?", value = FALSE, width = NULL))), 301 | br(),column(3,offset=0, downloadButton("downloadID", label = "Download problematic markers")) 302 | )), 303 | 304 | # === Separation 305 | fluidRow( column( 6,offset=3, hr())), 306 | 307 | # === Comparison graph 308 | column(11, offset=1, 309 | br(""), plotlyOutput("plot1" , height = "800px") 310 | ), 311 | 312 | 313 | # === 3 next widgets 314 | fluidRow(column(4,offset=2, style="opacity:0.9; color:grey","Custom colors / font" )), 315 | fluidRow(column(4,offset=2, style="opacity:0.9; color:grey", hr() )), 316 | fluidRow(column( 12, offset=0, 317 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 318 | column(3,offset=0, wellPanel( colourInput("my_color", "Line colour", "#BDA3CC", allowTransparent = TRUE))), 319 | column(3,offset=0, wellPanel( colourInput("my_color_bad", "Line colour (suspect marker)", "#9B9AA6", allowTransparent = TRUE))), 320 | column(3,offset=0, wellPanel( colourInput("my_color_name", "Map names colour", "#ED880C", allowTransparent = TRUE))), 321 | column(3,offset=0, wellPanel( sliderInput("thickness", "Line thickness:", min=0.1, max=12, value=2.0))) 322 | )), 323 | br(),br(),br(),br(),br(),br() 324 | 325 | #Close the tabPanel 326 | ), 327 | 328 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 329 | 330 | 331 | 332 | 333 | 334 | 335 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 336 | 337 | # ---------------------- 338 | # SHEET 4 : INTERCHROMOSOME ANALYSIS 339 | # ---------------------- 340 | tabPanel( 341 | 342 | #Name 343 | h4(legend4[1]), 344 | 345 | # ==== Title in Orange 346 | fluidRow(align="center", 347 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 348 | column(6,offset=3, 349 | br(), 350 | helpText( strong(legend4[2] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 351 | hr() 352 | )), 353 | 354 | 355 | # === Some text to explain the Figure: 356 | fluidRow(align="justify", 357 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 358 | column(6,offset=3, 359 | br(),legend4[3],br() 360 | ) 361 | ),br(),br(), 362 | 363 | 364 | # === Left column to choose input 365 | fluidRow(column(2, 366 | 367 | # Make som space 368 | br(""), br(""), 369 | 370 | # Choose the chromosome 371 | wellPanel(uiOutput("choose_chromo_sheet4")), 372 | br(), 373 | 374 | # Choix de la map1 375 | wellPanel(uiOutput("map1")), 376 | 377 | # Choix de la map2 378 | wellPanel(uiOutput("map2")), 379 | br(),br(),br(),br(),br() 380 | 381 | #Close column 382 | ), 383 | 384 | # === On the space left, I add the plot and color widget: 385 | column(8, 386 | 387 | br(), 388 | plotlyOutput("plot2" , height = "700px" , width = "900px"), 389 | 390 | br(),br() 391 | 392 | #Close column 393 | ), 394 | 395 | # === Key numbers 396 | column(2, 397 | br(), plotOutput("key_numbers_sheet_3", height = "500px" , width = "350px"), br(),br() 398 | )), 399 | 400 | 401 | # === Last line to choose colors 402 | fluidRow(column(4,offset=2, style="opacity:0.9; color:grey","Custom colors" )), 403 | fluidRow(column(4,offset=2, style="opacity:0.9; color:grey", hr() )), 404 | fluidRow(column( 9, offset=2, 405 | column(3, colourInput("col_s4_1", "Background Squares", "#BDA3CC", allowTransparent = TRUE)), 406 | column(3, colourInput("col_s4_2", "Markers", "#48444A", allowTransparent = TRUE)), 407 | column(3, colourInput("col_s4_3", "Markers (Interchromosome)", "#DB0913", allowTransparent = TRUE)), 408 | column(3, colourInput("col_s4_4", "Chromosome name", "#FFA500", allowTransparent = TRUE)) 409 | )), 410 | br(),br(),br(),br(),"",br(),br(),br(),"",br(),br(),br() 411 | 412 | 413 | 414 | 415 | 416 | #Close the tabPanel 417 | ), 418 | 419 | 420 | 421 | 422 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 423 | 424 | 425 | 426 | 427 | # ---------------------- 428 | # SHEET 5 : RAW MAP 429 | # ---------------------- 430 | tabPanel( 431 | 432 | #Name 433 | h4(legend5[1]), 434 | 435 | # ==== Title in Orange 436 | fluidRow(align="center", 437 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 438 | column(6,offset=3, 439 | br(), 440 | helpText( strong(legend5[6] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 441 | hr() 442 | )), 443 | 444 | # === Some text to explain the Figure: 445 | fluidRow(align="justify", 446 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 447 | column(6,offset=3, 448 | br(),legend5[2],legend5[5],br(),br(),legend5[7],br(), 449 | column(5,textInput("text_mark_remove", label = "", value = "Type marker name or pattern...")), 450 | column(5,radioButtons("keep_or_remove", "", choices = c("keep","remove"), selected =c("remove") , inline = T )) 451 | ), 452 | column(6,offset=3,helpText("e.g. '.*25.*' ", style="color:Grey") ) 453 | ),br(), 454 | 455 | 456 | # Left column to choose input 457 | column(2, 458 | 459 | # Make som space 460 | br(""), 461 | 462 | # Choix de la map 463 | wellPanel(uiOutput("choose_maps5")), 464 | 465 | # Choose chromosome 466 | br(""), 467 | wellPanel(uiOutput("choose_chromo_sheet5")) 468 | 469 | #Close column 470 | ), 471 | 472 | # On the space left, I add the plot 473 | column(6, offset=2, 474 | 475 | br(""), 476 | dataTableOutput('my_rough_map_viz' , width="500px") 477 | 478 | #Close column 479 | ),br(),br(),br(), 480 | 481 | # Legend of the plot 482 | column(2, 483 | br(),br(),br(),br(),br(),br() 484 | #,helpText(strong(p(legend5[2] , style="color:grey ; font-family: 'times'; font-size:12pt"))),br(),br(), 485 | #helpText(strong(p(legend5[5] , style="color:grey ; font-family: 'times'; font-size:12pt"))) 486 | #Close column 487 | ) 488 | 489 | 490 | 491 | #Close the tabPanel 492 | ), 493 | 494 | 495 | 496 | 497 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 509 | 510 | 511 | # ---------------------- 512 | # SHEET 6: DOCUMENTATION 513 | # ---------------------- 514 | tabPanel( 515 | 516 | #Name 517 | h4(legend6[1]), 518 | 519 | 520 | # ==== About section 521 | fluidRow(align="center", 522 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 523 | column(6,offset=3, 524 | br(), 525 | helpText( strong(legend6[2] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 526 | hr() 527 | )), 528 | fluidRow( 529 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 530 | column(3,offset=3, align="justify", 531 | br(), 532 | legend6[3],a("SNiPlay",href="http://sniplay.southgreen.fr/", target="_blank"),"[1] useful.", 533 | br() 534 | ), 535 | column(3,offset=0, align="justify", 536 | br(), 537 | legend6[4], 538 | br() 539 | )), br(),br(), 540 | 541 | 542 | 543 | 544 | 545 | # ==== Input files description 546 | fluidRow(align="center", 547 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 548 | column(6,offset=3, 549 | br(), 550 | helpText( strong(legend6[5] , style="color:Orange ; font-family: 'times'; font-size:30pt ; font-type:bold" )) , 551 | hr() 552 | )), 553 | fluidRow(align="justify", 554 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 555 | column(6,offset=3, 556 | br(), 557 | legend6[6], 558 | br() 559 | )), 560 | br(), 561 | fluidRow( 562 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 563 | column(3,offset=1,align="center", 564 | br(),br(), 565 | a("OneMap",href="https://cran.r-project.org/web/packages/onemap/onemap.pdf", target="_blank")," [2] format: 3 columns: linkage group, marker name and position in the map.", 566 | downloadLink("load_ex_format1", label = "Download"),br(), 567 | dataTableOutput('doc_ex1' , width="100px"), 568 | br() 569 | ), 570 | column(3,offset=1,align="center", 571 | br(), 572 | a("MapChart",href="http://jhered.oxfordjournals.org/content/93/1/77.full", target="_blank")," [3] format: composed of a sequence of linkage groups, each with a header specifying the linkage group title, followed by a sequence of lines with locus names and map positions.", 573 | downloadLink("load_ex_format2", label = "Download"), 574 | dataTableOutput('doc_ex2' , width="100px"), 575 | br() 576 | ), 577 | column(3,offset=1,align="center", 578 | br(), 579 | a("Carthagène",href="http://www7.inra.fr/mia/T/CarthaGene/", target="_blank"), " [4] format: 1 line only. Linkage groups are separated with {}. Then marker names and positions are provided successively. Output created with the \'mapget\' command.", 580 | downloadLink("load_ex_format3", label = "Download"),br(), 581 | dataTableOutput('doc_ex3' , width="300px"), 582 | br() 583 | ) 584 | 585 | ), br(), 586 | 587 | 588 | 589 | # ==== Use the app locally 590 | fluidRow(align="center", 591 | style="opacity: 1;background-color:white; margin-top: 0px;width: 100%;", 592 | column(6,offset=3, 593 | # Set the style of this page 594 | br(), 595 | helpText(strong(legend6[11] , style="color:orange ; font-family: 'times'; font-size:30pt ; font-type:bold" ) ) , 596 | hr() 597 | )), 598 | fluidRow(align="justify", 599 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 600 | column(6,offset=3, 601 | br(), 602 | legend6[12],br(),br(), 603 | legend6[13],a("Install R",href="https://www.r-project.org/", target="_blank"),legend6[16],br(),br(), 604 | legend6[14],br(), 605 | code("install.packages(shiny)"),br(), 606 | code("library(shiny)"),br(),br(), 607 | legend6[15],br(), 608 | code("runGitHub(\"GenMap-Comparator\",\"holtzy\")"),br(), 609 | br(), 610 | "see also the ", a("geneticMapComparator github repository", href="https://github.com/holtzy/GenMap-Comparator", target="_blank"),br() 611 | ) 612 | ), 613 | 614 | 615 | 616 | # ==== Sharing maps 617 | fluidRow(align="center", 618 | style="opacity: 1;background-color:white; margin-top: 0px;width: 100%;", 619 | column(6,offset=3, 620 | # Set the style of this page 621 | br(), 622 | helpText(strong(legend6[17] , style="color:orange ; font-family: 'times'; font-size:30pt ; font-type:bold" ) ) , 623 | hr() 624 | )), 625 | fluidRow(align="justify", 626 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 627 | column(6,offset=3, 628 | br(), 629 | legend6[18],br(),br(), 630 | br(),br() 631 | ) 632 | ), 633 | 634 | 635 | 636 | # ==== Contact 637 | fluidRow(align="center", 638 | style="opacity: 1;background-color:white; margin-top: 0px;width: 100%;", 639 | column(6,offset=3, 640 | # Set the style of this page 641 | br(), 642 | helpText(strong(legend6[10] , style="color:orange ; font-family: 'times'; font-size:30pt ; font-type:bold" ) ) , 643 | hr() 644 | )), 645 | fluidRow( align="center", 646 | style="opacity: 1;background-color:white; margin-top: 0px;width: 100%;", 647 | column(6,offset=3, 648 | img(src="map_montpellier.png" , width = 400), 649 | br() 650 | ), 651 | column(6,offset=3, 652 | br(), 653 | "Vincent Ranwez: ranwez@supagro.fr",br(), 654 | "Ge2pop Team, Bâtiment Arcad",br(), 655 | "10 rue Arthur Young",br(), 656 | "34090 MONTPELLIER, FRANCE",br() 657 | ) 658 | ), 659 | # fluidRow(column(6,offset=3,hr())) , br() , 660 | 661 | #Black line? 662 | #fluidRow( style=" opacity: 0.8 ; background-color: white ; margin-top: 0px ; width: 100%; " ), br(), 663 | 664 | 665 | # ==== References 666 | fluidRow(align="center", 667 | style="opacity: 1;background-color:white; margin-top: 0px;width: 100%;", 668 | column(6,offset=3, 669 | # Set the style of this page 670 | br(), 671 | helpText(strong("- References -" , style="color:orange ; font-family: 'times'; font-size:30pt ; font-type:bold" ) ) , 672 | hr() 673 | )), 674 | fluidRow(align="left", 675 | style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;", 676 | column(6,offset=3, 677 | br(), 678 | "1. Dereeper A., Nicolas S., Lecunff L., Bacilieri R., Doligez A., Peros JP., Ruiz M., This P. SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects.. BMC Bioinformatics. 2011. May 5;12(1):134. ", br(), 679 | "2. Margarido GRA, Souza AP, Garcia AAF. OneMap: software for genetic mapping in outcrossing species. Hereditas. Wiley Online Library; 2007;144: 78–79.", br(), 680 | "3. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, et al. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics. Elsevier; 1987;1: 174–181.", br(), 681 | "4. de Givry S, Bouchez M, Chabrier P, Milan D, Schiex T. CARTHAGENE: Multipopulation integrated genetic and radiation hybrid mapping. Bioinformatics. 2005;21: 1703–1704.", br(), 682 | br(), br() 683 | )), 684 | 685 | 686 | 687 | # === Last bandeau for the logos 688 | fluidRow( 689 | 690 | # Set the style of this page 691 | style=" opacity: 0.8 ; background-color: black ; margin-top: 0px ; width: 100%; ", 692 | 693 | # put the logos 694 | br(), 695 | column(2, offset=2, img(src="logo_INRAE.png", height = 40 ) , br(),br() ), 696 | column(2, offset=1, img(src="logo-institut-agro-montpellier.png", height = 40 ) ), 697 | column(2, offset=1, img(src="logo_arvalis.png", height = 40) ) 698 | #column(2, offset=1, img(src="logo_arvalis.png" , height = 70*grand, width = 110*grand) ) 699 | 700 | ) 701 | 702 | #Close the tabPanel 703 | ) 704 | 705 | # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 706 | 707 | 708 | 709 | 710 | 711 | #Close the shinyUI 712 | )) 713 | -------------------------------------------------------------------------------- /www/Rscript_for_background_image_sheet1: -------------------------------------------------------------------------------- 1 | #Make the background Image of the homepage 2 | #grand=60 3 | #png("www/my_image.png" , width = 40*grand, height = 22*grand) 4 | #par(bg="black" ) 5 | #my_colors=c(rgb(0.2,0.2,0.4,0.5), rgb(0.8,0.2,0.4,0.5), rgb(0.2,0.9,0.4,0.2) ) 6 | #library(MASS) 7 | #par(mar=c(0,0,0,0)) 8 | #my_iris=iris[,c(1,2,3,4,1,3,2,4,5)] 9 | #ze_colors=my_colors[as.numeric(my_iris$Species)] 10 | #par(cex.axis=2 , col.lab="white" , col.axis="grey") 11 | #parcoord(my_iris[,c(1:8)] , col= ze_colors , ylim=c(0.1,0.8) , xlim=c(3,7.5) ) 12 | #dev.off() 13 | 14 | -------------------------------------------------------------------------------- /www/genComp.css: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | /* Couleur du bandeau #463F32 */ 6 | .navbar-static-top { 7 | background-color: #000000; 8 | border-color: #000000; 9 | } 10 | 11 | 12 | /* Taille du bandeau */ 13 | .navbar { 14 | min-height: 62px; 15 | } 16 | 17 | /* mettre le titre de la page au dessus du menu de navigation */ 18 | .navbar-header { width:100%; background-color: #000000}.navbar-brand { width: 100%; text-align: center; font-family:times; font-size:28pt ; font-type:bold } 19 | /* Couleur message d'erreur*/ 20 | .shiny-output-error-validation { 21 | color: green; 22 | size: 225px ; 23 | padding-left: 25; 24 | } 25 | 26 | 27 | /* Couleur du fond 28 | body { 29 | background-image: url( http://papillondamour.p.a.pic.centerblog.net/fb850229.jpg ); 30 | background-color : white; 31 | background-attachment:fixed; 32 | background-repeat:repeat; 33 | } 34 | */ 35 | 36 | 37 | 38 | /* Couleur du fond */ 39 | body { 40 | background-color : white; 41 | } 42 | 43 | 44 | /* Juste pour le padding de la home-page */ 45 | .container-fluid { padding-left: 0; padding-right: 0; } 46 | 47 | 48 | /* Petite boxe pour sélectionner les data 49 | .shiny-input-container:not(.shiny-input-container-inline) { 50 | max-width: 13%; 51 | } 52 | */ 53 | 54 | 55 | 56 | /* CSS des lignes hr() (page de doc) */ 57 | hr { 58 | display: block; 59 | height: 3px; 60 | border: 0; 61 | border-top: 1px solid black; 62 | margin: 1em 0; 63 | padding: 0; 64 | } 65 | -------------------------------------------------------------------------------- /www/logo-institut-agro-montpellier.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/logo-institut-agro-montpellier.png -------------------------------------------------------------------------------- /www/logo_INRAE.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/logo_INRAE.png -------------------------------------------------------------------------------- /www/logo_INRA_old.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/logo_INRA_old.png -------------------------------------------------------------------------------- /www/logo_SUPAGRO.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/logo_SUPAGRO.jpg -------------------------------------------------------------------------------- /www/logo_arvalis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/logo_arvalis.png -------------------------------------------------------------------------------- /www/map_montpellier.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/map_montpellier.png -------------------------------------------------------------------------------- /www/my_image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/holtzy/GenMap-Comparator/efd2ac90d683595e73de8cf977b9da75fec5fac5/www/my_image.png --------------------------------------------------------------------------------