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the output of depth of coverage

Hi I have difficulty understanding the depth of coverage report from gatk. I the cumulative coverage count and cumulative coverage proportions, it says proprotions of loci with >= X coverage, aggregated over all bases, what does this over all bases mean? Besides, what does get 0-500 mean?
Please have a look at my output and help me understand it
gte_0 gte_1 gte_2 gte_3 gte_4 gte_5 gte_6 gte_7 gte_8 gte_9 gte_10 gte_11 gte_12 gte_13 gte_14 gte_15 gte_16 gte_17 gte_18 gte_19 gte_20 gte_21 gte_22 gte_23 gte_24 gte_25 gte_26 gte_27 gte_28 gte_29 gte_30 gte_31 gte_32 gte_33 gte_34 gte_35 gte_36 gte_37 gte_38 gte_39 gte_40 gte_41 gte_42 gte_43 gte_44 gte_45 gte_46 gte_47 gte_48 gte_49 gte_50 gte_51 gte_52 gte_53 gte_54 gte_55 gte_56 gte_57 gte_58 gte_59 gte_60 gte_61 gte_62 gte_63 gte_64 gte_65 gte_66 gte_67 gte_68 gte_69 gte_70 gte_71 gte_72 gte_73 gte_74 gte_75 gte_76 gte_77 gte_78 gte_79 gte_80 gte_81 gte_82 gte_83 gte_84 gte_85 gte_86 gte_87 gte_88 gte_89 gte_90 gte_91 gte_92 gte_93 gte_94 gte_95 gte_96 gte_97 gte_98 gte_99 gte_100 gte_101 gte_102 gte_103 gte_104 gte_105 gte_106 gte_107 gte_108 gte_109 gte_110 gte_111 gte_112 gte_113 gte_114 gte_115 gte_116 gte_117 gte_118 gte_119 gte_120 gte_121 gte_122 gte_123 gte_124 gte_125 gte_126 gte_127 gte_128 gte_129 gte_130 gte_131 gte_132 gte_133 gte_134 gte_135 gte_136 gte_137 gte_138 gte_139 gte_140 gte_141 gte_142 gte_143 gte_144 gte_145 gte_146 gte_147 gte_148 gte_149 gte_150 gte_151 gte_152 gte_153 gte_154 gte_155 gte_156 gte_157 gte_158 gte_159 gte_160 gte_161 gte_162 gte_163 gte_164 gte_165 gte_166 gte_167 gte_168 gte_169 gte_170 gte_171 gte_172 gte_173 gte_174 gte_175 gte_176 gte_177 gte_178 gte_179 gte_180 gte_181 gte_182 gte_183 gte_184 gte_185 gte_186 gte_187 gte_188 gte_189 gte_190 gte_191 gte_192 gte_193 gte_194 gte_195 gte_196 gte_197 gte_198 gte_199 gte_200 gte_201 gte_202 gte_203 gte_204 gte_205 gte_206 gte_207 gte_208 gte_209 gte_210 gte_211 gte_212 gte_213 gte_214 gte_215 gte_216 gte_217 gte_218 gte_219 gte_220 gte_221 gte_222 gte_223 gte_224 gte_225 gte_226 gte_227 gte_228 gte_229 gte_230 gte_231 gte_232 gte_233 gte_234 gte_235 gte_236 gte_237 gte_238 gte_239 gte_240 gte_241 gte_242 gte_243 gte_244 gte_245 gte_246 gte_247 gte_248 gte_249 gte_250 gte_251 gte_252 gte_253 gte_254 gte_255 gte_256 gte_257 gte_258 gte_259 gte_260 gte_261 gte_262 gte_263 gte_264 gte_265 gte_266 gte_267 gte_268 gte_269 gte_270 gte_271 gte_272 gte_273 gte_274 gte_275 gte_276 gte_277 gte_278 gte_279 gte_280 gte_281 gte_282 gte_283 gte_284 gte_285 gte_286 gte_287 gte_288 gte_289 gte_290 gte_291 gte_292 gte_293 gte_294 gte_295 gte_296 gte_297 gte_298 gte_299 gte_300 gte_301 gte_302 gte_303 gte_304 gte_305 gte_306 gte_307 gte_308 gte_309 gte_310 gte_311 gte_312 gte_313 gte_314 gte_315 gte_316 gte_317 gte_318 gte_319 gte_320 gte_321 gte_322 gte_323 gte_324 gte_325 gte_326 gte_327 gte_328 gte_329 gte_330 gte_331 gte_332 gte_333 gte_334 gte_335 gte_336 gte_337 gte_338 gte_339 gte_340 gte_341 gte_342 gte_343 gte_344 gte_345 gte_346 gte_347 gte_348 gte_349 gte_350 gte_351 gte_352 gte_353 gte_354 gte_355 gte_356 gte_357 gte_358 gte_359 gte_360 gte_361 gte_362 gte_363 gte_364 gte_365 gte_366 gte_367 gte_368 gte_369 gte_370 gte_371 gte_372 gte_373 gte_374 gte_375 gte_376 gte_377 gte_378 gte_379 gte_380 gte_381 gte_382 gte_383 gte_384 gte_385 gte_386 gte_387 gte_388 gte_389 gte_390 gte_391 gte_392 gte_393 gte_394 gte_395 gte_396 gte_397 gte_398 gte_399 gte_400 gte_401 gte_402 gte_403 gte_404 gte_405 gte_406 gte_407 gte_408 gte_409 gte_410 gte_411 gte_412 gte_413 gte_414 gte_415 gte_416 gte_417 gte_418 gte_419 gte_420 gte_421 gte_422 gte_423 gte_424 gte_425 gte_426 gte_427 gte_428 gte_429 gte_430 gte_431 gte_432 gte_433 gte_434 gte_435 gte_436 gte_437 gte_438 gte_439 gte_440 gte_441 gte_442 gte_443 gte_444 gte_445 gte_446 gte_447 gte_448 gte_449 gte_450 gte_451 gte_452 gte_453 gte_454 gte_455 gte_456 gte_457 gte_458 gte_459 gte_460 gte_461 gte_462 gte_463 gte_464 gte_465 gte_466 gte_467 gte_468 gte_469 gte_470 gte_471 gte_472 gte_473 gte_474 gte_475 gte_476 gte_477 gte_478 gte_479 gte_480 gte_481 gte_482 gte_483 gte_484 gte_485 gte_486 gte_487 gte_488 gte_489 gte_490 gte_491 gte_492 gte_493 gte_494 gte_495 gte_496 gte_497 gte_498 gte_499 gte_500
AA 1.00 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.91 0.90 0.88 0.86 0.84 0.83 0.81 0.79 0.78 0.77 0.75 0.74 0.73 0.72 0.71 0.70 0.69 0.68 0.67 0.66 0.66 0.65 0.64 0.63 0.63 0.62 0.62 0.61 0.60 0.60 0.59 0.59 0.58 0.58 0.57 0.57 0.56 0.56 0.56 0.55 0.55 0.54 0.54 0.54 0.53 0.53 0.53 0.52 0.52 0.52 0.51 0.51 0.51 0.50 0.50 0.50 0.49 0.49 0.49 0.49 0.48 0.48 0.48 0.47 0.47 0.47 0.47 0.46 0.46 0.46 0.46 0.45 0.45 0.45 0.45 0.44 0.44 0.44 0.44 0.44 0.43 0.43 0.43 0.43 0.42 0.42 0.42 0.42 0.42 0.41 0.41 0.41 0.41 0.41 0.40 0.40 0.40 0.40 0.40 0.39 0.39 0.39 0.39 0.39 0.39 0.38 0.38 0.38 0.38 0.38 0.37 0.37 0.37 0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.36 0.35 0.35 0.35 0.35 0.35 0.35 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.33 0.33 0.33 0.33 0.33 0.33 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
AE 1.00 0.91 0.88 0.86 0.83 0.81 0.79 0.77 0.76 0.74 0.72 0.71 0.70 0.68 0.67 0.66 0.65 0.64 0.63 0.62 0.62 0.61 0.60 0.60 0.59 0.58 0.58 0.57 0.57 0.56 0.55 0.55 0.54 0.54 0.54 0.53 0.53 0.52 0.52 0.51 0.51 0.51 0.50 0.50 0.50 0.49 0.49 0.49 0.48 0.48 0.48 0.47 0.47 0.47 0.46 0.46 0.46 0.46 0.45 0.45 0.45 0.44 0.44 0.44 0.44 0.43 0.43 0.43 0.43 0.42 0.42 0.42 0.42 0.42 0.41 0.41 0.41 0.41 0.40 0.40 0.40 0.40 0.40 0.39 0.39 0.39 0.39 0.38 0.38 0.38 0.38 0.38 0.37 0.37 0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.35 0.35 0.35 0.35 0.35 0.35 0.34 0.34 0.34 0.34 0.34 0.33 0.33 0.33 0.33 0.33 0.33 0.32 0.32 0.32 0.32 0.32 0.32 0.31 0.31 0.31 0.31 0.31 0.31 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.05
AI 1.00 0.96 0.92 0.89 0.86 0.83 0.81 0.79 0.77 0.75 0.73 0.71 0.70 0.69 0.67 0.66 0.65 0.64 0.63 0.62 0.61 0.60 0.60 0.59 0.58 0.58 0.57 0.56 0.56 0.55 0.55 0.54 0.54 0.53 0.53 0.52 0.52 0.51 0.51 0.51 0.50 0.50 0.50 0.49 0.49 0.48 0.48 0.48 0.47 0.47 0.47 0.47 0.46 0.46 0.46 0.45 0.45 0.45 0.45 0.44 0.44 0.44 0.43 0.43 0.43 0.43 0.42 0.42 0.42 0.42 0.42 0.41 0.41 0.41 0.41 0.40 0.40 0.40 0.40 0.39 0.39 0.39 0.39 0.39 0.38 0.38 0.38 0.38 0.38 0.37 0.37 0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.35 0.35 0.35 0.35 0.35 0.35 0.34 0.34 0.34 0.34 0.34 0.33 0.33 0.33 0.33 0.33 0.33 0.32 0.32 0.32 0.32 0.32 0.32 0.31 0.31 0.31 0.31 0.31 0.31 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.29 0.29 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.27 0.27 0.27 0.27 0.27 0.27 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Answers

  • yg1yg1 Member

    desides, for the same set of data, I have in the report summary Source_of_reads from_0_to_1) from_1_to_2) from_2_to_3) from_3_to_4) from_4_to_5) from_5_to_6) from_6_to_7) from_7_to_8) from_8_to_9) from_9_to_10) from_10_to_11) from_11_to_12) from_12_to_13) from_13_to_14) from_14_to_15) from_15_to_16) from_16_to_17) from_17_to_18) from_18_to_19) from_19_to_20) from_20_to_21) from_21_to_22) from_22_to_23) from_23_to_24) from_24_to_25) from_25_to_26) from_26_to_27) from_27_to_28) from_28_to_29) from_29_to_30) from_30_to_31) from_31_to_32) from_32_to_33) from_33_to_34) from_34_to_35) from_35_to_36) from_36_to_37) from_37_to_38) from_38_to_39) from_39_to_40) from_40_to_41) from_41_to_42) from_42_to_43) from_43_to_44) from_44_to_45) from_45_to_46) from_46_to_47) from_47_to_48) from_48_to_49) from_49_to_50) from_50_to_51) from_51_to_52) from_52_to_53) from_53_to_54) from_54_to_55) from_55_to_56) from_56_to_57) from_57_to_58) from_58_to_59) from_59_to_60) from_60_to_61) from_61_to_62) from_62_to_63) from_63_to_64) from_64_to_65) from_65_to_66) from_66_to_67) from_67_to_68) from_68_to_69) from_69_to_70) from_70_to_71) from_71_to_72) from_72_to_73) from_73_to_74) from_74_to_75) from_75_to_76) from_76_to_77) from_77_to_78) from_78_to_79) from_79_to_80) from_80_to_81) from_81_to_82) from_82_to_83) from_83_to_84) from_84_to_85) from_85_to_86) from_86_to_87) from_87_to_88) from_88_to_89) from_89_to_90) from_90_to_91) from_91_to_92) from_92_to_93) from_93_to_94) from_94_to_95) from_95_to_96) from_96_to_97) from_97_to_98) from_98_to_99) from_99_to_100) from_100_to_101) from_101_to_102) from_102_to_103) from_103_to_104) from_104_to_105) from_105_to_106) from_106_to_107) from_107_to_108) from_108_to_109) from_109_to_110) from_110_to_111) from_111_to_112) from_112_to_113) from_113_to_114) from_114_to_115) from_115_to_116) from_116_to_117) from_117_to_118) from_118_to_119) from_119_to_120) from_120_to_121) from_121_to_122) from_122_to_123) from_123_to_124) from_124_to_125) from_125_to_126) from_126_to_127) from_127_to_128) from_128_to_129) from_129_to_130) from_130_to_131) from_131_to_132) from_132_to_133) from_133_to_134) from_134_to_135) from_135_to_136) from_136_to_137) from_137_to_138) from_138_to_139) from_139_to_140) from_140_to_141) from_141_to_142) from_142_to_143) from_143_to_144) from_144_to_145) from_145_to_146) from_146_to_147) from_147_to_148) from_148_to_149) from_149_to_150) from_150_to_151) from_151_to_152) from_152_to_153) from_153_to_154) from_154_to_155) from_155_to_156) from_156_to_157) from_157_to_158) from_158_to_159) from_159_to_160) from_160_to_161) from_161_to_162) from_162_to_163) from_163_to_164) from_164_to_165) from_165_to_166) from_166_to_167) from_167_to_168) from_168_to_169) from_169_to_170) from_170_to_171) from_171_to_172) from_172_to_173) from_173_to_174) from_174_to_175) from_175_to_176) from_176_to_177) from_177_to_178) from_178_to_179) from_179_to_180) from_180_to_181) from_181_to_182) from_182_to_183) from_183_to_184) from_184_to_185) from_185_to_186) from_186_to_187) from_187_to_188) from_188_to_189) from_189_to_190) from_190_to_191) from_191_to_192) from_192_to_193) from_193_to_194) from_194_to_195) from_195_to_196) from_196_to_197) from_197_to_198) from_198_to_199) from_199_to_200) from_200_to_201) from_201_to_202) from_202_to_203) from_203_to_204) from_204_to_205) from_205_to_206) from_206_to_207) from_207_to_208) from_208_to_209) from_209_to_210) from_210_to_211) from_211_to_212) from_212_to_213) from_213_to_214) from_214_to_215) from_215_to_216) from_216_to_217) from_217_to_218) from_218_to_219) from_219_to_220) from_220_to_221) from_221_to_222) from_222_to_223) from_223_to_224) from_224_to_225) from_225_to_226) from_226_to_227) from_227_to_228) from_228_to_229) from_229_to_230) from_230_to_231) from_231_to_232) from_232_to_233) from_233_to_234) from_234_to_235) from_235_to_236) from_236_to_237) from_237_to_238) from_238_to_239) from_239_to_240) from_240_to_241) from_241_to_242) from_242_to_243) from_243_to_244) from_244_to_245) from_245_to_246) from_246_to_247) from_247_to_248) from_248_to_249) from_249_to_250) from_250_to_251) from_251_to_252) from_252_to_253) from_253_to_254) from_254_to_255) from_255_to_256) from_256_to_257) from_257_to_258) from_258_to_259) from_259_to_260) from_260_to_261) from_261_to_262) from_262_to_263) from_263_to_264) from_264_to_265) from_265_to_266) from_266_to_267) from_267_to_268) from_268_to_269) from_269_to_270) from_270_to_271) from_271_to_272) from_272_to_273) from_273_to_274) from_274_to_275) from_275_to_276) from_276_to_277) from_277_to_278) from_278_to_279) from_279_to_280) from_280_to_281) from_281_to_282) from_282_to_283) from_283_to_284) from_284_to_285) from_285_to_286) from_286_to_287) from_287_to_288) from_288_to_289) from_289_to_290) from_290_to_291) from_291_to_292) from_292_to_293) from_293_to_294) from_294_to_295) from_295_to_296) from_296_to_297) from_297_to_298) from_298_to_299) from_299_to_300) from_300_to_301) from_301_to_302) from_302_to_303) from_303_to_304) from_304_to_305) from_305_to_306) from_306_to_307) from_307_to_308) from_308_to_309) from_309_to_310) from_310_to_311) from_311_to_312) from_312_to_313) from_313_to_314) from_314_to_315) from_315_to_316) from_316_to_317) from_317_to_318) from_318_to_319) from_319_to_320) from_320_to_321) from_321_to_322) from_322_to_323) from_323_to_324) from_324_to_325) from_325_to_326) from_326_to_327) from_327_to_328) from_328_to_329) from_329_to_330) from_330_to_331) from_331_to_332) from_332_to_333) from_333_to_334) from_334_to_335) from_335_to_336) from_336_to_337) from_337_to_338) from_338_to_339) from_339_to_340) from_340_to_341) from_341_to_342) from_342_to_343) from_343_to_344) from_344_to_345) from_345_to_346) from_346_to_347) from_347_to_348) from_348_to_349) from_349_to_350) from_350_to_351) from_351_to_352) from_352_to_353) from_353_to_354) from_354_to_355) from_355_to_356) from_356_to_357) from_357_to_358) from_358_to_359) from_359_to_360) from_360_to_361) from_361_to_362) from_362_to_363) from_363_to_364) from_364_to_365) from_365_to_366) from_366_to_367) from_367_to_368) from_368_to_369) from_369_to_370) from_370_to_371) from_371_to_372) from_372_to_373) from_373_to_374) from_374_to_375) from_375_to_376) from_376_to_377) from_377_to_378) from_378_to_379) from_379_to_380) from_380_to_381) from_381_to_382) from_382_to_383) from_383_to_384) from_384_to_385) from_385_to_386) from_386_to_387) from_387_to_388) from_388_to_389) from_389_to_390) from_390_to_391) from_391_to_392) from_392_to_393) from_393_to_394) from_394_to_395) from_395_to_396) from_396_to_397) from_397_to_398) from_398_to_399) from_399_to_400) from_400_to_401) from_401_to_402) from_402_to_403) from_403_to_404) from_404_to_405) from_405_to_406) from_406_to_407) from_407_to_408) from_408_to_409) from_409_to_410) from_410_to_411) from_411_to_412) from_412_to_413) from_413_to_414) from_414_to_415) from_415_to_416) from_416_to_417) from_417_to_418) from_418_to_419) from_419_to_420) from_420_to_421) from_421_to_422) from_422_to_423) from_423_to_424) from_424_to_425) from_425_to_426) from_426_to_427) from_427_to_428) from_428_to_429) from_429_to_430) from_430_to_431) from_431_to_432) from_432_to_433) from_433_to_434) from_434_to_435) from_435_to_436) from_436_to_437) from_437_to_438) from_438_to_439) from_439_to_440) from_440_to_441) from_441_to_442) from_442_to_443) from_443_to_444) from_444_to_445) from_445_to_446) from_446_to_447) from_447_to_448) from_448_to_449) from_449_to_450) from_450_to_451) from_451_to_452) from_452_to_453) from_453_to_454) from_454_to_455) from_455_to_456) from_456_to_457) from_457_to_458) from_458_to_459) from_459_to_460) from_460_to_461) from_461_to_462) from_462_to_463) from_463_to_464) from_464_to_465) from_465_to_466) from_466_to_467) from_467_to_468) from_468_to_469) from_469_to_470) from_470_to_471) from_471_to_472) from_472_to_473) from_473_to_474) from_474_to_475) from_475_to_476) from_476_to_477) from_477_to_478) from_478_to_479) from_479_to_480) from_480_to_481) from_481_to_482) from_482_to_483) from_483_to_484) from_484_to_485) from_485_to_486) from_486_to_487) from_487_to_488) from_488_to_489) from_489_to_490) from_490_to_491) from_491_to_492) from_492_to_493) from_493_to_494) from_494_to_495) from_495_to_496) from_496_to_497) from_497_to_498) from_498_to_499) from_499_to_500) from_500_to_inf
    sample_AA 351668 631058 677303 707428 743999 787198 850400 929180 1014823 1079412 1118641 1115280 1091098 1044882 992891 935861 883138 827282 780370 737218 695875 656443 623983 594516 562831 540280 513744 492710 469532 451317 432214 417067 399375 386980 371704 360270 349359 339432 328204 319662 311564 301371 292184 284933 277432 270916 265463 258930 253894 247193 241246 236401 231114 226661 221239 216610 211599 208133 204386 200089 196072 193475 190293 187218 184543 181673 179254 175719 173149 169174 168844 165977 164604 163037 160423 158504 156831 155712 153799 152928 150994 149458 148397 147468 145528 145033 142878 141059 139775 138417 137799 136568 135244 134355 133515 131617 131324 129426 129446 127196 126230 125085 124951 123579 122668 121930 120801 120274 119417 118156 117987 117953 116400 115819 115226 114013 113238 112638 111847 111566 110807 110336 110139 109403 108230 108185 106335 106551 105757 105456 104551 104425 102883 102548 102211 101600 101177 100893 100007 99444 98393 98630 97534 97776 97111 97064 96613 96429 96013 95761 94977 94363 93807 93516 93137 92275 91971 91252 91235 90359 90006 90473 89773 89361 89599 89172 88768 88900 87832 87571 86742 86607 86527 86298 85526 85181 84744 84638 83821 83605 83413 83451 82919 83261 82849 82810 82221 82431 81827 81965 80943 79903 79726 79471 79038 78202 78323 78219 77393 77041 76714 76436 75713 74922 74315 73821 72762 71836 71248 71042 70051 69341 69046 68901 68538 67289 66872 66539 65591 64087 64203 63316 63017 62290 61544 61176 61695 60716 59937 59258 59373 58586 57887 57897 57287 56885 56581 55784 55438 54803 54607 53868 53742 53190 52925 52886 53174 52136 52047 52013 51147 50719 50956 50295 50256 56260 49614 49443 48764 48448 48678 48430 48418 48383 47843 48045 47075 46913 46942 46465 46414 46526 46038 46243 46201 45732 45782 45257 45057 45220 44815 44927 44423 44192 44322 44018 43592 43531 43329 43208 42878 43011 42886 42258 41946 42709 42057 41961 41706 41248 41480 40875 41077 41186 40887 40710 40685 40239 40353 40426 40395 39952 39936 39547 39674 39518 38996 39281 38952 38704 38795 39040 38654 38401 38267 38333 38227 37883 37948 37751 37623 37249 37211 37233 36970 36975 36957 36759 36790 36809 36487 36438 36106 36348 36496 36444 35883 35884 35941 35740 35713 35862 35414 35441 35584 35752 35171 35868 35360 35086 35110 35548 34842 35446 34909 35398 35164 35234 34584 34881 34609 34839 34408 34422 34469 34242 34298 34273 34154 33964 33832 33876 34045 33581 33547 33654 33417 33487 33327 33285 33332 32593 33076 32885 33235 32503 32659 32549 32716 32687 32503 32359 31938 32220 31989 31885 31981 31507 31834 31705 31897 31788 31836 31450 31862 31450 31491 31282 31193 31103 30873 30742 30708 30626 30313 30233 30069 30115 29845 29983 29775 30076 29886 30081 29862 29586 29941 29584 29150 28959 28789 29065 28849 29274 28887 29045 28899 28790 28431 28569 28225 28488 28174 27997 27797 27547 27529 27065 27254 27007 27164 26704 26875 26665 26765 26612 26420 26419 26010 26142 25715 25995 25987 25815 25391 25629 25549 25911 25639 25318 25251 25306 24917 24663 24852 24599 24772 24652 24331 24253 24499 24338 23751 24138 23755 23780 23755 23716 23715 23303 4459880

  • yg1yg1 Member

    I don't understand the gte 500 and the from 500's difference. Thank you. Because I have a large number at gte from 500.

  • Geraldine_VdAuweraGeraldine_VdAuwera Cambridge, MAMember, Administrator, Broadie admin

    "gte" stands for "greater than or equal to". So the first table gives you the proportion of reads in each bin of the histogram, e.g. 100% of sites have coverage greater than or equal to 0; 99% of sites have coverage greater than or equal to 1, and so on until 500 (which is the default endpoint). The second table gives you the number of loci (genome positions) in each bin, e.g. there are 351668 loci that are covered by 0 to 1 reads, 631058 loci that are covered by 1 to 2 reads, etc. if I remember correctly.

  • yg1yg1 Member

    Thank you for the explanation. Is the "100% of sites" the percent of contigs (i'm using an assembly as reference) have coverage over a certain percentage? That means the depth of coverage was calculated for each contig, then the number of contigs meeting a certain creteria(such as >=5) were divided by the total number of contigs to get this percentage? How is the depth of coverage calculated for each contig?
    I'm confused because I used another software to calculate it the got a much lower number...Both for the average cov and the percentile.

  • yg1yg1 Member
    edited March 2014

    Another confusing thing is that when I looked at the output,the number seems to be the number of reads, so it is calculated based on the number of reads. Since the mun and the percentage report should be co-ordinate, I'm confused about what the "sites" means. Because I only have 67813 contigs, where does the 351668 come from?
    Source_of_reads from_0_to_1)
    sample_AA 351668
    sample_AE 5479474
    sample_AI 2746676

  • Geraldine_VdAuweraGeraldine_VdAuwera Cambridge, MAMember, Administrator, Broadie admin

    No, the numbers in these tables are per nucleotide position in the genome, not per contig. To get numbers per contig you have to pass a list of intervals if I remember correctly.

  • yg1yg1 Member
    edited March 2014

    Can you explain it in more detail about what is per nucleotide position? Can I understan it like this- I have 675813 contigs, and the nucleotide position I have from 0 to 1 is 63843258, which is the total length of contigs add up together. thus I got 94(63843258/675813), which is my ave contig length? But that seems too short.Or it is the length of mapped reads?

  • Geraldine_VdAuweraGeraldine_VdAuwera Cambridge, MAMember, Administrator, Broadie admin

    These tables give you the coverage distribution based on the total length of all contigs added up together.

    What kind of genome are you working with? Is this a microbial draft genome? In any case this is not the right way to look at contig size in your dataset, because it's unlikely to have a normal distribution. You should plot the distribution to visualize the quality of your assembly. And I wouldn't recommend trying to assess the average coverage per contig based on these tables, because it is likely that many small contigs have very different coverage compared to the bigger contigs.

  • yg1yg1 Member

    Hi Geraldine,
    The total length add together (63,843,258) is not the same from our calculation(2,375,258,173), It made me even more confused when looked at the summary: sample_id total, AA 9047558667, shouldn't it be 63843258(get0)? What does the total mean here in the summary? I'm working on a plant tramscriptom assembly.It did gave me a mean cov 141.7, does this mean my average cov is 141.7?

  • yg1yg1 Member

    Sorry, I think the total length (nucleotide position) from GATK's output is correct, it matches with the output from another software. But the average depth is dramatically diff ,which have the coverage mean coverageData = 37.23X
    std coverageData = 96.38X, while gatk give a number of mean=141.72...

  • Geraldine_VdAuweraGeraldine_VdAuwera Cambridge, MAMember, Administrator, Broadie admin

    I can't comment on the results you're getting with different programs, they may be calculating things differently. And it's especially hard to comment on isolated numbers, as opposed to seeing the full output from the program.

  • yg1yg1 Member

    Thanks, currently I'll stick with the gatk results.

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    @Geraldine_VdAuwera
    Hi,
    In depth of coverage output file: "SAMPLES.sample_cummulative_coverage_counts", the output appears something like this:
    gte_0 gte_1 gte_2 gte_3 gte_4 gte_5 [ so on....]
    NSamples_1 2900338458 2899457776 2898947737 2898506831 2898086718 2897651034 [ so on....]
    NSamples_2 2900338458 2898922190 2898202735 2897578622 2896975276 2896356575 [ so on....]
    NSamples_3 2900338458 2898295747 2897363481 2896569112 2895787250 2894980514 [ so on....]
    NSamples_4 2900338458 2897521393 2896320777 2895303066 2894296893 2893255223 [ so on....]
    NSamples_5 2900338458 2896682667 2895232238 2893942904 2892619447 2891186998 [ so on....]
    NSamples_6 2900338458 2876597832 2873006800 2871059506 2869375499 2867721798 [ so on....]
    NSamples_7 2900338458 2873436252 2870739681 2868661547 2866725881 2864787657 [ so on....]
    NSamples_8 2900338458 2870922855 2867840684 2865395811 2863098344 2860744084 [ so on....]
    NSamples_9 2900338458 2867753803 2864118291 2861105041 2858157462 2855044945 [ so on....]
    NSamples_10 2900338458 2860712865 2855762252 2851459788 2846886203 2841472018 [ so on....]

    how to know from which sample these counts belongs to (as determined by input file order or something else) ???

    i am curious because the order of sample details in "SAMPLES.sample_summary" is not in accordance with the input file...

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    @Geraldine_VdAuwera
    Hi,
    Could you please answer this..?

  • Geraldine_VdAuweraGeraldine_VdAuwera Cambridge, MAMember, Administrator, Broadie admin
    If I recall correctly the first field on each row is the sample name as identified in the bams by the RG:SM tag. So the counts on each line belong to that line's sample.
  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    @Geraldine_VdAuwera
    yes.. but in this case the sample names are not displaying for ""SAMPLES.sample_cummulative_coverage_counts"" file...

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    Only the samples are distinguished by NSamples_1, NSamples_2 , ...

    But not with their real names identified in the bams.

    On the other hand, another file generated by same run of DepthofCoverage "SAMPLES.sample_summary" has sample identifiers from bams..

  • SheilaSheila Broad InstituteMember, Broadie, Moderator admin

    @MUHAMMADSOHAILRAZA
    Hi,

    Can you confirm you are using the latest version? I think there was a fix that went in for this recently.

    -Sheila

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    @Sheila Yes i used the latest version GATK-3.6..

  • SheilaSheila Broad InstituteMember, Broadie, Moderator admin

    @MUHAMMADSOHAILRAZA
    Hi,

    The cumulative coverage counts file is not supposed to differentiate samples. It tells you how many sample sites have greater than or equal to the depth in question. So, NSamples_1 tells you if at least 1 sample has the depth. NSamples_2 tells you if at least 2 samples has the depth...and so on.

    -Sheila

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭

    @Sheila Hi,

    Thanks for your reply.
    I tried to plot these values on graph and try to have a look if if at least 1 sample has the depth then it would be N_SAMPLE_1,

    But in graph N_SAMPLE_1 (cumulative depth) > N_SAMPLE_10, Why, Please see attached file... and could you please explain in bit more detail about the purpose of these counts for different samples..

    Regards

  • SheilaSheila Broad InstituteMember, Broadie, Moderator admin
    edited July 2016

    @MUHAMMADSOHAILRAZA
    Hi,

    Perhaps an example is the best way to explain this.
    Let's say I have two samples: sample 1 and sample 2.
    I run DepthOfCoverage on those two samples at 1 site only.
    Sample 1 has depth of 3 at the site, and sample 2 has depth of 4 at the site.

    My output for the cumulative coverage counts file would look like this:

    gte_1 gte_2 gte_3 gte_4
    Nsample_1 1 1 1 1
    Nsample_2 1 1 1 0

    This tells you how many sites have greater than or equal to the depth in question in each of the samples.

    I hope this helps.

    -Sheila

  • MUHAMMADSOHAILRAZAMUHAMMADSOHAILRAZA Beijing Institute of Genomics, CASMember ✭✭
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