J. Taroni 2018

In response to comments, we wish to provide a bit more background information about the recount2 compendium. To do so, we’ll gather information from the following sources:

Download MetaSRA data

wget -N -O data/sample_info/metasra.v1-4.json http://metasra.biostat.wisc.edu/static/metasra_versions/v1.4/metasra.v1-4.json
WARNING: timestamping does nothing in combination with -O. See the manual
for details.

--2018-11-29 15:45:23--  http://metasra.biostat.wisc.edu/static/metasra_versions/v1.4/metasra.v1-4.json
Resolving metasra.biostat.wisc.edu (metasra.biostat.wisc.edu)... 128.104.140.173
Connecting to metasra.biostat.wisc.edu (metasra.biostat.wisc.edu)|128.104.140.173|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 64744709 (62M) [application/json]
Saving to: ‘data/sample_info/metasra.v1-4.json’

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 13500K .......... .......... .......... .......... .......... 21% 2.30M 40s
 13550K .......... .......... .......... .......... .......... 21% 1.33M 40s
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 13700K .......... .......... .......... .......... .......... 21% 2.46M 40s
 13750K .......... .......... .......... .......... .......... 21% 4.45M 40s
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 14150K .......... .......... .......... .......... .......... 22% 4.64M 39s
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 14250K .......... .......... .......... .......... .......... 22% 2.46M 38s
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 14350K .......... .......... .......... .......... .......... 22% 3.24M 38s
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 15250K .......... .......... .......... .......... .......... 24% 1.87M 36s
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 15400K .......... .......... .......... .......... .......... 24% 3.86M 36s
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 15600K .......... .......... .......... .......... .......... 24% 1.68M 36s
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 15800K .......... .......... .......... .......... .......... 25% 1.01M 36s
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 15950K .......... .......... .......... .......... .......... 25% 1.02M 36s
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 16750K .......... .......... .......... .......... .......... 26% 3.43M 37s
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 16900K .......... .......... .......... .......... .......... 26% 1.65M 37s
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 17050K .......... .......... .......... .......... .......... 27% 2.21M 37s
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 17350K .......... .......... .......... .......... .......... 27% 1.20M 36s
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 17700K .......... .......... .......... .......... .......... 28% 1.26M 36s
 17750K .......... .......... .......... .......... .......... 28% 1010K 36s
 17800K .......... .......... .......... .......... .......... 28% 1.42M 36s
 17850K .......... .......... .......... .......... .......... 28% 1.28M 36s
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 17950K .......... .......... .......... .......... .......... 28% 2.68M 36s
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 18050K .......... .......... .......... .......... .......... 28% 1.25M 36s
 18100K .......... .......... .......... .......... .......... 28%  746K 36s
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 18200K .......... .......... .......... .......... .......... 28% 1.78M 36s
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 18950K .......... .......... .......... .......... .......... 30% 1.80M 35s
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 19350K .......... .......... .......... .......... .......... 30% 1.87M 34s
 19400K .......... .......... .......... .......... .......... 30% 2.59M 34s
 19450K .......... .......... .......... .......... .......... 30% 3.21M 34s
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 19750K .......... .......... .......... .......... .......... 31% 1.81M 34s
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 20300K .......... .......... .......... .......... .......... 32% 2.86M 33s
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 20700K .......... .......... .......... .......... .......... 32% 1.08M 35s
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 25800K .......... .......... .......... .......... .......... 40% 67.0K 32s
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 28050K .......... .......... .......... .......... .......... 44%  476K 31s
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 28150K .......... .......... .......... .......... .......... 44%  649K 31s
 28200K .......... .......... .......... .......... .......... 44%  504K 31s
 28250K .......... .......... .......... .......... .......... 44%  510K 31s
 28300K .......... .......... .......... .......... .......... 44%  509K 31s
 28350K .......... .......... .......... .......... .......... 44%  467K 31s
 28400K .......... .......... .......... .......... .......... 44% 1.29M 31s
 28450K .......... .......... .......... .......... .......... 45%  518K 31s
 28500K .......... .......... .......... .......... .......... 45%  755K 31s
 28550K .......... .......... .......... .......... .......... 45% 1.55M 31s
 28600K .......... .......... .......... .......... .......... 45%  893K 31s
 28650K .......... .......... .......... .......... .......... 45% 1.14M 31s
 28700K .......... .......... .......... .......... .......... 45% 1.10M 31s
 28750K .......... .......... .......... .......... .......... 45% 1.12M 31s
 28800K .......... .......... .......... .......... .......... 45% 1.96M 31s
 28850K .......... .......... .......... .......... .......... 45%  861K 31s
 28900K .......... .......... .......... .......... .......... 45% 1.01M 31s
 28950K .......... .......... .......... .......... .......... 45%  920K 31s
 29000K .......... .......... .......... .......... .......... 45% 1.19M 31s
 29050K .......... .......... .......... .......... .......... 46% 1.03M 31s
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 29150K .......... .......... .......... .......... .......... 46%  640K 31s
 29200K .......... .......... .......... .......... .......... 46% 1.91M 31s
 29250K .......... .......... .......... .......... .......... 46% 1.24M 31s
 29300K .......... .......... .......... .......... .......... 46% 1.37M 31s
 29350K .......... .......... .......... .......... .......... 46% 1.20M 31s
 29400K .......... .......... .......... .......... .......... 46% 1.15M 30s
 29450K .......... .......... .......... .......... .......... 46% 1.45M 30s
 29500K .......... .......... .......... .......... .......... 46% 1.36M 30s
 29550K .......... .......... .......... .......... .......... 46% 1.56M 30s
 29600K .......... .......... .......... .......... .......... 46% 1.17M 30s
 29650K .......... .......... .......... .......... .......... 46% 1.86M 30s
 29700K .......... .......... .......... .......... .......... 47% 1.22M 30s
 29750K .......... .......... .......... .......... .......... 47% 1.52M 30s
 29800K .......... .......... .......... .......... .......... 47% 1.41M 30s
 29850K .......... .......... .......... .......... .......... 47% 1.37M 30s
 29900K .......... .......... .......... .......... .......... 47% 1.36M 30s
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 30000K .......... .......... .......... .......... .......... 47% 1.07M 30s
 30050K .......... .......... .......... .......... .......... 47% 1.42M 30s
 30100K .......... .......... .......... .......... .......... 47% 1.52M 30s
 30150K .......... .......... .......... .......... .......... 47% 1.62M 30s
 30200K .......... .......... .......... .......... .......... 47% 1.23M 30s
 30250K .......... .......... .......... .......... .......... 47% 1.36M 30s
 30300K .......... .......... .......... .......... .......... 48% 1.37M 29s
 30350K .......... .......... .......... .......... .......... 48% 1.44M 29s
 30400K .......... .......... .......... .......... .......... 48% 1.60M 29s
 30450K .......... .......... .......... .......... .......... 48% 1.76M 29s
 30500K .......... .......... .......... .......... .......... 48% 1.50M 29s
 30550K .......... .......... .......... .......... .......... 48% 1.15M 29s
 30600K .......... .......... .......... .......... .......... 48% 1.12M 29s
 30650K .......... .......... .......... .......... .......... 48% 1.21M 29s
 30700K .......... .......... .......... .......... .......... 48% 1.55M 29s
 30750K .......... .......... .......... .......... .......... 48% 1.34M 29s
 30800K .......... .......... .......... .......... .......... 48% 1.23M 29s
 30850K .......... .......... .......... .......... .......... 48% 1.42M 29s
 30900K .......... .......... .......... .......... .......... 48% 1.57M 29s
 30950K .......... .......... .......... .......... .......... 49% 1.33M 29s
 31000K .......... .......... .......... .......... .......... 49% 1.30M 29s
 31050K .......... .......... .......... .......... .......... 49% 1.45M 29s
 31100K .......... .......... .......... .......... .......... 49% 1.22M 29s
 31150K .......... .......... .......... .......... .......... 49% 1.17M 29s
 31200K .......... .......... .......... .......... .......... 49%  958K 29s
 31250K .......... .......... .......... .......... .......... 49%  643K 29s
 31300K .......... .......... .......... .......... .......... 49%  678K 28s
 31350K .......... .......... .......... .......... .......... 49%  479K 29s
 31400K .......... .......... .......... .......... .......... 49%  680K 28s
 31450K .......... .......... .......... .......... .......... 49%  442K 29s
 31500K .......... .......... .......... .......... .......... 49%  114K 29s
 31550K .......... .......... .......... .......... .......... 49%  839K 29s
 31600K .......... .......... .......... .......... .......... 50%  544K 29s
 31650K .......... .......... .......... .......... .......... 50%  644K 29s
 31700K .......... .......... .......... .......... .......... 50%  965K 29s
 31750K .......... .......... .......... .......... .......... 50%  980K 29s
 31800K .......... .......... .......... .......... .......... 50% 1.05M 29s
 31850K .......... .......... .......... .......... .......... 50%  582K 29s
 31900K .......... .......... .......... .......... .......... 50%  724K 29s
 31950K .......... .......... .......... .......... .......... 50%  572K 29s
 32000K .......... .......... .......... .......... .......... 50%  530K 29s
 32050K .......... .......... .......... .......... .......... 50%  818K 29s
 32100K .......... .......... .......... .......... .......... 50%  504K 29s
 32150K .......... .......... .......... .......... .......... 50%  481K 29s
 32200K .......... .......... .......... .......... .......... 51%  495K 29s
 32250K .......... .......... .......... .......... .......... 51%  894K 29s
 32300K .......... .......... .......... .......... .......... 51%  590K 29s
 32350K .......... .......... .......... .......... .......... 51%  867K 29s
 32400K .......... .......... .......... .......... .......... 51%  843K 29s
 32450K .......... .......... .......... .......... .......... 51% 1.18M 28s
 32500K .......... .......... .......... .......... .......... 51%  621K 28s
 32550K .......... .......... .......... .......... .......... 51% 1.47M 28s
 32600K .......... .......... .......... .......... .......... 51%  907K 28s
 32650K .......... .......... .......... .......... .......... 51%  485K 28s
 32700K .......... .......... .......... .......... .......... 51% 1.01M 28s
 32750K .......... .......... .......... .......... .......... 51%  877K 28s
 32800K .......... .......... .......... .......... .......... 51% 1.02M 28s
 32850K .......... .......... .......... .......... .......... 52%  523K 28s
 32900K .......... .......... .......... .......... .......... 52%  989K 28s
 32950K .......... .......... .......... .......... .......... 52% 1018K 28s
 33000K .......... .......... .......... .......... .......... 52%  784K 28s
 33050K .......... .......... .......... .......... .......... 52% 1005K 28s
 33100K .......... .......... .......... .......... .......... 52%  995K 28s
 33150K .......... .......... .......... .......... .......... 52% 1.18M 28s
 33200K .......... .......... .......... .......... .......... 52%  629K 28s
 33250K .......... .......... .......... .......... .......... 52% 1.38M 28s
 33300K .......... .......... .......... .......... .......... 52% 2.13M 28s
 33350K .......... .......... .......... .......... .......... 52% 2.23M 28s
 33400K .......... .......... .......... .......... .......... 52% 4.12M 28s
 33450K .......... .......... .......... .......... .......... 52% 3.20M 28s
 33500K .......... .......... .......... .......... .......... 53% 2.76M 28s
 33550K .......... .......... .......... .......... .......... 53% 1.76M 28s
 33600K .......... .......... .......... .......... .......... 53% 2.97M 27s
 33650K .......... .......... .......... .......... .......... 53% 2.40M 27s
 33700K .......... .......... .......... .......... .......... 53% 2.50M 27s
 33750K .......... .......... .......... .......... .......... 53% 2.49M 27s
 33800K .......... .......... .......... .......... .......... 53% 3.17M 27s
 33850K .......... .......... .......... .......... .......... 53% 2.05M 27s
 33900K .......... .......... .......... .......... .......... 53% 2.46M 27s
 33950K .......... .......... .......... .......... .......... 53% 2.99M 27s
 34000K .......... .......... .......... .......... .......... 53% 1.22M 27s
 34050K .......... .......... .......... .......... .......... 53% 2.71M 27s
 34100K .......... .......... .......... .......... .......... 54% 2.54M 27s
 34150K .......... .......... .......... .......... .......... 54% 2.19M 27s
 34200K .......... .......... .......... .......... .......... 54% 2.93M 27s
 34250K .......... .......... .......... .......... .......... 54% 2.08M 27s
 34300K .......... .......... .......... .......... .......... 54% 2.49M 26s
 34350K .......... .......... .......... .......... .......... 54% 3.01M 26s
 34400K .......... .......... .......... .......... .......... 54% 2.88M 26s
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 34500K .......... .......... .......... .......... .......... 54% 3.25M 26s
 34550K .......... .......... .......... .......... .......... 54% 2.69M 26s
 34600K .......... .......... .......... .......... .......... 54% 3.16M 26s
 34650K .......... .......... .......... .......... .......... 54% 2.56M 26s
 34700K .......... .......... .......... .......... .......... 54% 2.47M 26s
 34750K .......... .......... .......... .......... .......... 55% 2.75M 26s
 34800K .......... .......... .......... .......... .......... 55% 3.62M 26s
 34850K .......... .......... .......... .......... .......... 55% 2.75M 26s
 34900K .......... .......... .......... .......... .......... 55% 2.75M 26s
 34950K .......... .......... .......... .......... .......... 55% 2.79M 26s
 35000K .......... .......... .......... .......... .......... 55% 2.08M 26s
 35050K .......... .......... .......... .......... .......... 55% 1.93M 25s
 35100K .......... .......... .......... .......... .......... 55% 2.95M 25s
 35150K .......... .......... .......... .......... .......... 55% 2.65M 25s
 35200K .......... .......... .......... .......... .......... 55% 3.22M 25s
 35250K .......... .......... .......... .......... .......... 55% 2.50M 25s
 35300K .......... .......... .......... .......... .......... 55% 2.69M 25s
 35350K .......... .......... .......... .......... .......... 55% 2.95M 25s
 35400K .......... .......... .......... .......... .......... 56% 2.94M 25s
 35450K .......... .......... .......... .......... .......... 56% 2.73M 25s
 35500K .......... .......... .......... .......... .......... 56% 2.40M 25s
 35550K .......... .......... .......... .......... .......... 56% 2.80M 25s
 35600K .......... .......... .......... .......... .......... 56% 3.68M 25s
 35650K .......... .......... .......... .......... .......... 56% 2.93M 25s
 35700K .......... .......... .......... .......... .......... 56% 3.22M 25s
 35750K .......... .......... .......... .......... .......... 56% 3.03M 25s
 35800K .......... .......... .......... .......... .......... 56% 3.34M 24s
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 35900K .......... .......... .......... .......... .......... 56% 2.63M 24s
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 36000K .......... .......... .......... .......... .......... 57% 4.53M 24s
 36050K .......... .......... .......... .......... .......... 57% 3.55M 24s
 36100K .......... .......... .......... .......... .......... 57% 3.02M 24s
 36150K .......... .......... .......... .......... .......... 57% 2.87M 24s
 36200K .......... .......... .......... .......... .......... 57% 2.21M 24s
 36250K .......... .......... .......... .......... .......... 57% 2.86M 24s
 36300K .......... .......... .......... .......... .......... 57% 3.43M 24s
 36350K .......... .......... .......... .......... .......... 57% 3.19M 24s
 36400K .......... .......... .......... .......... .......... 57% 3.26M 24s
 36450K .......... .......... .......... .......... .......... 57% 3.02M 24s
 36500K .......... .......... .......... .......... .......... 57% 2.10M 24s
 36550K .......... .......... .......... .......... .......... 57% 3.30M 23s
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 36650K .......... .......... .......... .......... .......... 58% 3.21M 23s
 36700K .......... .......... .......... .......... .......... 58%  863K 23s
 36750K .......... .......... .......... .......... .......... 58% 2.87M 23s
 36800K .......... .......... .......... .......... .......... 58% 3.41M 23s
 36850K .......... .......... .......... .......... .......... 58% 2.63M 23s
 36900K .......... .......... .......... .......... .......... 58% 3.31M 23s
 36950K .......... .......... .......... .......... .......... 58% 2.22M 23s
 37000K .......... .......... .......... .......... .......... 58% 2.69M 23s
 37050K .......... .......... .......... .......... .......... 58% 3.02M 23s
 37100K .......... .......... .......... .......... .......... 58% 2.39M 23s
 37150K .......... .......... .......... .......... .......... 58% 3.02M 23s
 37200K .......... .......... .......... .......... .......... 58% 2.66M 23s
 37250K .......... .......... .......... .......... .......... 58% 2.08M 23s
 37300K .......... .......... .......... .......... .......... 59% 3.16M 23s
 37350K .......... .......... .......... .......... .......... 59% 2.92M 23s
 37400K .......... .......... .......... .......... .......... 59% 2.53M 22s
 37450K .......... .......... .......... .......... .......... 59% 3.00M 22s
 37500K .......... .......... .......... .......... .......... 59% 2.96M 22s
 37550K .......... .......... .......... .......... .......... 59% 3.84M 22s
 37600K .......... .......... .......... .......... .......... 59% 1.57M 22s
 37650K .......... .......... .......... .......... .......... 59% 2.63M 22s
 37700K .......... .......... .......... .......... .......... 59% 2.30M 22s
 37750K .......... .......... .......... .......... .......... 59% 3.34M 22s
 37800K .......... .......... .......... .......... .......... 59% 2.92M 22s
 37850K .......... .......... .......... .......... .......... 59% 3.01M 22s
 37900K .......... .......... .......... .......... .......... 60% 2.82M 22s
 37950K .......... .......... .......... .......... .......... 60% 3.92M 22s
 38000K .......... .......... .......... .......... .......... 60% 2.68M 22s
 38050K .......... .......... .......... .......... .......... 60% 2.86M 22s
 38100K .......... .......... .......... .......... .......... 60% 3.24M 22s
 38150K .......... .......... .......... .......... .......... 60% 2.46M 22s
 38200K .......... .......... .......... .......... .......... 60% 1.99M 21s
 38250K .......... .......... .......... .......... .......... 60% 2.41M 21s
 38300K .......... .......... .......... .......... .......... 60% 3.01M 21s
 38350K .......... .......... .......... .......... .......... 60% 2.13M 21s
 38400K .......... .......... .......... .......... .......... 60% 3.27M 21s
 38450K .......... .......... .......... .......... .......... 60% 2.61M 21s
 38500K .......... .......... .......... .......... .......... 60% 2.90M 21s
 38550K .......... .......... .......... .......... .......... 61% 3.15M 21s
 38600K .......... .......... .......... .......... .......... 61% 3.30M 21s
 38650K .......... .......... .......... .......... .......... 61% 2.97M 21s
 38700K .......... .......... .......... .......... .......... 61% 2.94M 21s
 38750K .......... .......... .......... .......... .......... 61% 3.23M 21s
 38800K .......... .......... .......... .......... .......... 61% 3.80M 21s
 38850K .......... .......... .......... .......... .......... 61% 2.74M 21s
 38900K .......... .......... .......... .......... .......... 61% 2.71M 21s
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 39000K .......... .......... .......... .......... .......... 61% 3.18M 21s
 39050K .......... .......... .......... .......... .......... 61% 2.94M 20s
 39100K .......... .......... .......... .......... .......... 61% 2.78M 20s
 39150K .......... .......... .......... .......... .......... 61% 3.13M 20s
 39200K .......... .......... .......... .......... .......... 62% 3.15M 20s
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 39300K .......... .......... .......... .......... .......... 62% 3.37M 20s
 39350K .......... .......... .......... .......... .......... 62% 2.78M 20s
 39400K .......... .......... .......... .......... .......... 62% 3.21M 20s
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 39550K .......... .......... .......... .......... .......... 62% 2.96M 20s
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 39850K .......... .......... .......... .......... .......... 63% 1.71M 20s
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 39950K .......... .......... .......... .......... .......... 63% 1.64M 19s
 40000K .......... .......... .......... .......... .......... 63% 2.08M 19s
 40050K .......... .......... .......... .......... .......... 63% 1.59M 19s
 40100K .......... .......... .......... .......... .......... 63% 1.71M 19s
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 40750K .......... .......... .......... .......... .......... 64%  943K 19s
 40800K .......... .......... .......... .......... .......... 64% 1.03M 19s
 40850K .......... .......... .......... .......... .......... 64%  915K 19s
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 40950K .......... .......... .......... .......... .......... 64% 1022K 19s
 41000K .......... .......... .......... .......... .......... 64% 1.28M 19s
 41050K .......... .......... .......... .......... .......... 65% 1.13M 18s
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 41200K .......... .......... .......... .......... .......... 65% 1.13M 18s
 41250K .......... .......... .......... .......... .......... 65% 1003K 18s
 41300K .......... .......... .......... .......... .......... 65% 1.18M 18s
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 41500K .......... .......... .......... .......... .......... 65% 1.28M 18s
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 41600K .......... .......... .......... .......... .......... 65% 1005K 18s
 41650K .......... .......... .......... .......... .......... 65% 1.18M 18s
 41700K .......... .......... .......... .......... .......... 66%  958K 18s
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 41900K .......... .......... .......... .......... .......... 66% 1.27M 18s
 41950K .......... .......... .......... .......... .......... 66%  755K 18s
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 42100K .......... .......... .......... .......... .......... 66% 1.65M 18s
 42150K .......... .......... .......... .......... .......... 66% 2.55M 18s
 42200K .......... .......... .......... .......... .......... 66% 3.22M 18s
 42250K .......... .......... .......... .......... .......... 66% 2.97M 18s
 42300K .......... .......... .......... .......... .......... 66% 3.59M 17s
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 42650K .......... .......... .......... .......... .......... 67% 3.24M 17s
 42700K .......... .......... .......... .......... .......... 67% 2.78M 17s
 42750K .......... .......... .......... .......... .......... 67% 3.42M 17s
 42800K .......... .......... .......... .......... .......... 67% 2.87M 17s
 42850K .......... .......... .......... .......... .......... 67% 2.49M 17s
 42900K .......... .......... .......... .......... .......... 67% 3.55M 17s
 42950K .......... .......... .......... .......... .......... 68% 3.49M 17s
 43000K .......... .......... .......... .......... .......... 68% 3.57M 17s
 43050K .......... .......... .......... .......... .......... 68% 3.16M 17s
 43100K .......... .......... .......... .......... .......... 68% 4.32M 17s
 43150K .......... .......... .......... .......... .......... 68% 3.89M 17s
 43200K .......... .......... .......... .......... .......... 68% 3.92M 16s
 43250K .......... .......... .......... .......... .......... 68% 2.90M 16s
 43300K .......... .......... .......... .......... .......... 68% 2.39M 16s
 43350K .......... .......... .......... .......... .......... 68% 2.93M 16s
 43400K .......... .......... .......... .......... .......... 68% 2.85M 16s
 43450K .......... .......... .......... .......... .......... 68% 3.35M 16s
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 43600K .......... .......... .......... .......... .......... 69% 2.60M 16s
 43650K .......... .......... .......... .......... .......... 69% 2.49M 16s
 43700K .......... .......... .......... .......... .......... 69% 2.50M 16s
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 43800K .......... .......... .......... .......... .......... 69% 1.96M 16s
 43850K .......... .......... .......... .......... .......... 69% 2.94M 16s
 43900K .......... .......... .......... .......... .......... 69% 3.25M 16s
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 44000K .......... .......... .......... .......... .......... 69% 3.14M 16s
 44050K .......... .......... .......... .......... .......... 69% 4.32M 16s
 44100K .......... .......... .......... .......... .......... 69% 1.73M 16s
 44150K .......... .......... .......... .......... .......... 69% 3.38M 16s
 44200K .......... .......... .......... .......... .......... 69% 1.40M 15s
 44250K .......... .......... .......... .......... .......... 70% 2.87M 15s
 44300K .......... .......... .......... .......... .......... 70% 2.52M 15s
 44350K .......... .......... .......... .......... .......... 70% 3.60M 15s
 44400K .......... .......... .......... .......... .......... 70% 2.27M 15s
 44450K .......... .......... .......... .......... .......... 70% 3.82M 15s
 44500K .......... .......... .......... .......... .......... 70% 2.89M 15s
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 44650K .......... .......... .......... .......... .......... 70% 3.15M 15s
 44700K .......... .......... .......... .......... .......... 70% 3.61M 15s
 44750K .......... .......... .......... .......... .......... 70% 3.03M 15s
 44800K .......... .......... .......... .......... .......... 70% 2.94M 15s
 44850K .......... .......... .......... .......... .......... 71% 3.86M 15s
 44900K .......... .......... .......... .......... .......... 71% 2.58M 15s
 44950K .......... .......... .......... .......... .......... 71% 1.11M 15s
 45000K .......... .......... .......... .......... .......... 71% 4.38M 15s
 45050K .......... .......... .......... .......... .......... 71% 4.13M 15s
 45100K .......... .......... .......... .......... .......... 71% 5.29M 15s
 45150K .......... .......... .......... .......... .......... 71% 3.84M 15s
 45200K .......... .......... .......... .......... .......... 71% 3.55M 14s
 45250K .......... .......... .......... .......... .......... 71% 2.06M 14s
 45300K .......... .......... .......... .......... .......... 71% 2.79M 14s
 45350K .......... .......... .......... .......... .......... 71% 3.68M 14s
 45400K .......... .......... .......... .......... .......... 71% 3.15M 14s
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 45500K .......... .......... .......... .......... .......... 72% 3.07M 14s
 45550K .......... .......... .......... .......... .......... 72% 4.06M 14s
 45600K .......... .......... .......... .......... .......... 72% 3.42M 14s
 45650K .......... .......... .......... .......... .......... 72%  187K 14s
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 46100K .......... .......... .......... .......... .......... 72%  189M 14s
 46150K .......... .......... .......... .......... .......... 73%  228M 14s
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 46250K .......... .......... .......... .......... .......... 73%  214M 13s
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 46400K .......... .......... .......... .......... .......... 73% 1.12M 13s
 46450K .......... .......... .......... .......... .......... 73% 2.71M 13s
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 46600K .......... .......... .......... .......... .......... 73% 3.40M 13s
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 46700K .......... .......... .......... .......... .......... 73% 2.66M 13s
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 46800K .......... .......... .......... .......... .......... 74% 2.67M 13s
 46850K .......... .......... .......... .......... .......... 74% 2.89M 13s
 46900K .......... .......... .......... .......... .......... 74% 3.97M 13s
 46950K .......... .......... .......... .......... .......... 74% 2.36M 13s
 47000K .......... .......... .......... .......... .......... 74% 3.21M 13s
 47050K .......... .......... .......... .......... .......... 74% 3.27M 13s
 47100K .......... .......... .......... .......... .......... 74% 4.25M 13s
 47150K .......... .......... .......... .......... .......... 74% 2.77M 13s
 47200K .......... .......... .......... .......... .......... 74% 4.05M 13s
 47250K .......... .......... .......... .......... .......... 74% 2.71M 12s
 47300K .......... .......... .......... .......... .......... 74% 3.42M 12s
 47350K .......... .......... .......... .......... .......... 74% 2.48M 12s
 47400K .......... .......... .......... .......... .......... 75% 2.26M 12s
 47450K .......... .......... .......... .......... .......... 75% 1.73M 12s
 47500K .......... .......... .......... .......... .......... 75% 2.17M 12s
 47550K .......... .......... .......... .......... .......... 75% 2.04M 12s
 47600K .......... .......... .......... .......... .......... 75% 2.13M 12s
 47650K .......... .......... .......... .......... .......... 75% 2.00M 12s
 47700K .......... .......... .......... .......... .......... 75% 2.42M 12s
 47750K .......... .......... .......... .......... .......... 75% 2.63M 12s
 47800K .......... .......... .......... .......... .......... 75% 3.32M 12s
 47850K .......... .......... .......... .......... .......... 75% 2.01M 12s
 47900K .......... .......... .......... .......... .......... 75% 1.47M 12s
 47950K .......... .......... .......... .......... .......... 75% 1.77M 12s
 48000K .......... .......... .......... .......... .......... 75% 2.12M 12s
 48050K .......... .......... .......... .......... .......... 76% 1.40M 12s
 48100K .......... .......... .......... .......... .......... 76% 1.37M 12s
 48150K .......... .......... .......... .......... .......... 76% 1.25M 12s
 48200K .......... .......... .......... .......... .......... 76% 1.49M 12s
 48250K .......... .......... .......... .......... .......... 76% 2.87M 12s
 48300K .......... .......... .......... .......... .......... 76% 2.46M 12s
 48350K .......... .......... .......... .......... .......... 76% 2.81M 12s
 48400K .......... .......... .......... .......... .......... 76% 3.40M 11s
 48450K .......... .......... .......... .......... .......... 76% 3.54M 11s
 48500K .......... .......... .......... .......... .......... 76% 3.16M 11s
 48550K .......... .......... .......... .......... .......... 76% 3.24M 11s
 48600K .......... .......... .......... .......... .......... 76% 3.47M 11s
 48650K .......... .......... .......... .......... .......... 77% 4.06M 11s
 48700K .......... .......... .......... .......... .......... 77% 4.01M 11s
 48750K .......... .......... .......... .......... .......... 77% 1.93M 11s
 48800K .......... .......... .......... .......... .......... 77% 3.51M 11s
 48850K .......... .......... .......... .......... .......... 77% 3.03M 11s
 48900K .......... .......... .......... .......... .......... 77% 2.62M 11s
 48950K .......... .......... .......... .......... .......... 77% 3.04M 11s
 49000K .......... .......... .......... .......... .......... 77% 2.94M 11s
 49050K .......... .......... .......... .......... .......... 77% 3.28M 11s
 49100K .......... .......... .......... .......... .......... 77% 3.71M 11s
 49150K .......... .......... .......... .......... .......... 77% 3.95M 11s
 49200K .......... .......... .......... .......... .......... 77% 3.72M 11s
 49250K .......... .......... .......... .......... .......... 77% 3.15M 11s
 49300K .......... .......... .......... .......... .......... 78% 3.27M 11s
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 49400K .......... .......... .......... .......... .......... 78% 3.20M 11s
 49450K .......... .......... .......... .......... .......... 78% 4.49M 11s
 49500K .......... .......... .......... .......... .......... 78% 3.44M 10s
 49550K .......... .......... .......... .......... .......... 78% 2.11M 10s
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 49750K .......... .......... .......... .......... .......... 78% 1.58M 10s
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 49900K .......... .......... .......... .......... .......... 79% 1.73M 10s
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 50000K .......... .......... .......... .......... .......... 79% 1.42M 10s
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 50100K .......... .......... .......... .......... .......... 79% 1.95M 10s
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 50200K .......... .......... .......... .......... .......... 79% 1.41M 10s
 50250K .......... .......... .......... .......... .......... 79% 2.27M 10s
 50300K .......... .......... .......... .......... .......... 79% 1.70M 10s
 50350K .......... .......... .......... .......... .......... 79% 3.35M 10s
 50400K .......... .......... .......... .......... .......... 79% 2.65M 10s
 50450K .......... .......... .......... .......... .......... 79% 1.46M 10s
 50500K .......... .......... .......... .......... .......... 79% 1.81M 10s
 50550K .......... .......... .......... .......... .......... 80% 1.49M 10s
 50600K .......... .......... .......... .......... .......... 80% 2.18M 10s
 50650K .......... .......... .......... .......... .......... 80%  913K 10s
 50700K .......... .......... .......... .......... .......... 80% 1.28M 10s
 50750K .......... .......... .......... .......... .......... 80% 1.07M 9s
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 50850K .......... .......... .......... .......... .......... 80% 1.24M 9s
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 51100K .......... .......... .......... .......... .......... 80%  711K 9s
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 51850K .......... .......... .......... .......... .......... 82% 58.3K 9s
 51900K .......... .......... .......... .......... .......... 82%  312M 9s
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 52100K .......... .......... .......... .......... .......... 82%  328M 9s
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 52400K .......... .......... .......... .......... .......... 82% 4.36M 9s
 52450K .......... .......... .......... .......... .......... 83% 75.8M 9s
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 52550K .......... .......... .......... .......... .......... 83%  344M 8s
 52600K .......... .......... .......... .......... .......... 83% 3.16M 8s
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 52900K .......... .......... .......... .......... .......... 83% 1.27M 8s
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 53050K .......... .......... .......... .......... .......... 83% 2.26M 8s
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 53150K .......... .......... .......... .......... .......... 84% 3.70M 8s
 53200K .......... .......... .......... .......... .......... 84% 2.48M 8s
 53250K .......... .......... .......... .......... .......... 84% 1.38M 8s
 53300K .......... .......... .......... .......... .......... 84% 2.87M 8s
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 53500K .......... .......... .......... .......... .......... 84%  662K 8s
 53550K .......... .......... .......... .......... .......... 84% 1.03M 8s
 53600K .......... .......... .......... .......... .......... 84% 1.12M 8s
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 53750K .......... .......... .......... .......... .......... 85% 1.27M 7s
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 54100K .......... .......... .......... .......... .......... 85% 1.24M 7s
 54150K .......... .......... .......... .......... .......... 85% 1.41M 7s
 54200K .......... .......... .......... .......... .......... 85% 1.30M 7s
 54250K .......... .......... .......... .......... .......... 85% 1.38M 7s
 54300K .......... .......... .......... .......... .......... 85% 1.47M 7s
 54350K .......... .......... .......... .......... .......... 86% 1.12M 7s
 54400K .......... .......... .......... .......... .......... 86% 1.46M 7s
 54450K .......... .......... .......... .......... .......... 86% 2.35M 7s
 54500K .......... .......... .......... .......... .......... 86% 1.17M 7s
 54550K .......... .......... .......... .......... .......... 86% 1.41M 7s
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 54750K .......... .......... .......... .......... .......... 86% 1.55M 7s
 54800K .......... .......... .......... .......... .......... 86%  621K 7s
 54850K .......... .......... .......... .......... .......... 86%  965K 7s
 54900K .......... .......... .......... .......... .......... 86%  594K 7s
 54950K .......... .......... .......... .......... .......... 86% 1.01M 7s
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 55150K .......... .......... .......... .......... .......... 87%  600K 6s
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 55300K .......... .......... .......... .......... .......... 87% 75.1K 6s
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 55600K .......... .......... .......... .......... .......... 88% 1.78M 6s
 55650K .......... .......... .......... .......... .......... 88%  965K 6s
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 55800K .......... .......... .......... .......... .......... 88% 1018K 6s
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 55900K .......... .......... .......... .......... .......... 88% 1.14M 6s
 55950K .......... .......... .......... .......... .......... 88%  996K 6s
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 56050K .......... .......... .......... .......... .......... 88% 1.08M 6s
 56100K .......... .......... .......... .......... .......... 88% 1.25M 6s
 56150K .......... .......... .......... .......... .......... 88%  759K 6s
 56200K .......... .......... .......... .......... .......... 88% 1015K 6s
 56250K .......... .......... .......... .......... .......... 89% 1.10M 6s
 56300K .......... .......... .......... .......... .......... 89%  600K 6s
 56350K .......... .......... .......... .......... .......... 89% 1.30M 6s
 56400K .......... .......... .......... .......... .......... 89%  944K 6s
 56450K .......... .......... .......... .......... .......... 89% 1.22M 6s
 56500K .......... .......... .......... .......... .......... 89% 1.25M 5s
 56550K .......... .......... .......... .......... .......... 89% 1.17M 5s
 56600K .......... .......... .......... .......... .......... 89% 1.47M 5s
 56650K .......... .......... .......... .......... .......... 89% 1.27M 5s
 56700K .......... .......... .......... .......... .......... 89% 1.49M 5s
 56750K .......... .......... .......... .......... .......... 89% 1.20M 5s
 56800K .......... .......... .......... .......... .......... 89% 1.33M 5s
 56850K .......... .......... .......... .......... .......... 89%  966K 5s
 56900K .......... .......... .......... .......... .......... 90%  905K 5s
 56950K .......... .......... .......... .......... .......... 90%  870K 5s
 57000K .......... .......... .......... .......... .......... 90%  745K 5s
 57050K .......... .......... .......... .......... .......... 90%  578K 5s
 57100K .......... .......... .......... .......... .......... 90% 1.01M 5s
 57150K .......... .......... .......... .......... .......... 90% 1.08M 5s
 57200K .......... .......... .......... .......... .......... 90% 1.11M 5s
 57250K .......... .......... .......... .......... .......... 90%  902K 5s
 57300K .......... .......... .......... .......... .......... 90% 2.15M 5s
 57350K .......... .......... .......... .......... .......... 90% 1.24M 5s
 57400K .......... .......... .......... .......... .......... 90% 1.55M 5s
 57450K .......... .......... .......... .......... .......... 90% 1.16M 5s
 57500K .......... .......... .......... .......... .......... 91% 1.24M 5s
 57550K .......... .......... .......... .......... .......... 91% 1.88M 5s
 57600K .......... .......... .......... .......... .......... 91%  965K 5s
 57650K .......... .......... .......... .......... .......... 91%  971K 5s
 57700K .......... .......... .......... .......... .......... 91% 1.54M 5s
 57750K .......... .......... .......... .......... .......... 91% 1.30M 4s
 57800K .......... .......... .......... .......... .......... 91% 2.25M 4s
 57850K .......... .......... .......... .......... .......... 91% 2.08M 4s
 57900K .......... .......... .......... .......... .......... 91% 1.94M 4s
 57950K .......... .......... .......... .......... .......... 91% 2.12M 4s
 58000K .......... .......... .......... .......... .......... 91% 1.81M 4s
 58050K .......... .......... .......... .......... .......... 91% 1.26M 4s
 58100K .......... .......... .......... .......... .......... 91% 1.06M 4s
 58150K .......... .......... .......... .......... .......... 92% 1.08M 4s
 58200K .......... .......... .......... .......... .......... 92% 1.36M 4s
 58250K .......... .......... .......... .......... .......... 92% 1.38M 4s
 58300K .......... .......... .......... .......... .......... 92% 1.43M 4s
 58350K .......... .......... .......... .......... .......... 92% 1015K 4s
 58400K .......... .......... .......... .......... .......... 92% 1.22M 4s
 58450K .......... .......... .......... .......... .......... 92% 1.26M 4s
 58500K .......... .......... .......... .......... .......... 92% 1.59M 4s
 58550K .......... .......... .......... .......... .......... 92% 1.56M 4s
 58600K .......... .......... .......... .......... .......... 92% 2.03M 4s
 58650K .......... .......... .......... .......... .......... 92% 1.84M 4s
 58700K .......... .......... .......... .......... .......... 92% 1.39M 4s
 58750K .......... .......... .......... .......... .......... 92%  287K 4s
 58800K .......... .......... .......... .......... .......... 93% 1.45M 4s
 58850K .......... .......... .......... .......... .......... 93% 1.51M 4s
 58900K .......... .......... .......... .......... .......... 93% 1.44M 4s
 58950K .......... .......... .......... .......... .......... 93% 1.10M 3s
 59000K .......... .......... .......... .......... .......... 93% 1.98M 3s
 59050K .......... .......... .......... .......... .......... 93% 2.57M 3s
 59100K .......... .......... .......... .......... .......... 93% 1.24M 3s
 59150K .......... .......... .......... .......... .......... 93% 1.84M 3s
 59200K .......... .......... .......... .......... .......... 93% 1.70M 3s
 59250K .......... .......... .......... .......... .......... 93% 1.27M 3s
 59300K .......... .......... .......... .......... .......... 93% 1.24M 3s
 59350K .......... .......... .......... .......... .......... 93% 1.26M 3s
 59400K .......... .......... .......... .......... .......... 94% 1.53M 3s
 59450K .......... .......... .......... .......... .......... 94% 2.24M 3s
 59500K .......... .......... .......... .......... .......... 94% 2.03M 3s
 59550K .......... .......... .......... .......... .......... 94% 1.44M 3s
 59600K .......... .......... .......... .......... .......... 94% 1.54M 3s
 59650K .......... .......... .......... .......... .......... 94% 1.38M 3s
 59700K .......... .......... .......... .......... .......... 94% 1.39M 3s
 59750K .......... .......... .......... .......... .......... 94%  716K 3s
 59800K .......... .......... .......... .......... .......... 94%  623K 3s
 59850K .......... .......... .......... .......... .......... 94%  522K 3s
 59900K .......... .......... .......... .......... .......... 94%  859K 3s
 59950K .......... .......... .......... .......... .......... 94%  592K 3s
 60000K .......... .......... .......... .......... .......... 94%  813K 3s
 60050K .......... .......... .......... .......... .......... 95%  705K 3s
 60100K .......... .......... .......... .......... .......... 95%  971K 3s
 60150K .......... .......... .......... .......... .......... 95%  806K 3s
 60200K .......... .......... .......... .......... .......... 95%  962K 2s
 60250K .......... .......... .......... .......... .......... 95%  928K 2s
 60300K .......... .......... .......... .......... .......... 95% 1.33M 2s
 60350K .......... .......... .......... .......... .......... 95%  921K 2s
 60400K .......... .......... .......... .......... .......... 95% 2.81M 2s
 60450K .......... .......... .......... .......... .......... 95% 1.49M 2s
 60500K .......... .......... .......... .......... .......... 95%  831K 2s
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 60600K .......... .......... .......... .......... .......... 95%  854K 2s
 60650K .......... .......... .......... .......... .......... 96%  750K 2s
 60700K .......... .......... .......... .......... .......... 96%  903K 2s
 60750K .......... .......... .......... .......... .......... 96%  729K 2s
 60800K .......... .......... .......... .......... .......... 96%  442K 2s
 60850K .......... .......... .......... .......... .......... 96%  632K 2s
 60900K .......... .......... .......... .......... .......... 96%  661K 2s
 60950K .......... .......... .......... .......... .......... 96%  659K 2s
 61000K .......... .......... .......... .......... .......... 96%  843K 2s
 61050K .......... .......... .......... .......... .......... 96% 1.06M 2s
 61100K .......... .......... .......... .......... .......... 96%  874K 2s
 61150K .......... .......... .......... .......... .......... 96% 1.62M 2s
 61200K .......... .......... .......... .......... .......... 96% 3.82M 2s
 61250K .......... .......... .......... .......... .......... 96% 4.49M 2s
 61300K .......... .......... .......... .......... .......... 97% 2.97M 2s
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 61400K .......... .......... .......... .......... .......... 97% 3.20M 1s
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 61800K .......... .......... .......... .......... .......... 97% 2.54M 1s
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 62000K .......... .......... .......... .......... .......... 98% 3.31M 1s
 62050K .......... .......... .......... .......... .......... 98% 4.22M 1s
 62100K .......... .......... .......... .......... .......... 98% 3.72M 1s
 62150K .......... .......... .......... .......... .......... 98% 4.95M 1s
 62200K .......... .......... .......... .......... .......... 98% 3.32M 1s
 62250K .......... .......... .......... .......... .......... 98% 3.63M 1s
 62300K .......... .......... .......... .......... .......... 98% 3.90M 1s
 62350K .......... .......... .......... .......... .......... 98% 3.56M 1s
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 62500K .......... .......... .......... .......... .......... 98% 4.26M 1s
 62550K .......... .......... .......... .......... .......... 99% 3.84M 1s
 62600K .......... .......... .......... .......... .......... 99% 3.65M 0s
 62650K .......... .......... .......... .......... .......... 99% 2.64M 0s
 62700K .......... .......... .......... .......... .......... 99% 4.10M 0s
 62750K .......... .......... .......... .......... .......... 99% 3.54M 0s
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 62850K .......... .......... .......... .......... .......... 99% 3.45M 0s
 62900K .......... .......... .......... .......... .......... 99% 3.27M 0s
 62950K .......... .......... .......... .......... .......... 99% 2.87M 0s
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 63050K .......... .......... .......... .......... .......... 99% 3.53M 0s
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 63150K .......... .......... .......... .......... .......... 99% 3.31M 0s
 63200K .......... .......... .......                         100% 1.70M=51s

2018-11-29 15:46:15 (1.20 MB/s) - ‘data/sample_info/metasra.v1-4.json’ saved [64744709/64744709]

Libraries, functions

# BiocInstaller::biocLite("SRAdb")
# library(SRAdb)
# magrittr pipe
`%>%` <- dplyr::`%>%`

Custom function for converting between the sample (e.g., SRS) accession codes and the sample names in recount2 which follow this pattern: SRPxxxxxx.SRRxxxxx

ConvertToRecountSampleName <- function(sample.vector, conversion.df) {
  # Takes a vector of sample accession codes and converts them to sample/column
  # names for use with recount2 expression matrices
  # 
  # Args:
  #   sample.vector: a vector of sample accession codes
  #   conversion.df: a data.frame that provides the mappings between study,
  #                  sample, and run accession codes -- must have "run", 
  #                  "study", and "sample" column names
  # 
  # Returns:
  #   sample.names: a vector of sample/column names for use with recount2 --
  #                 these are the study and run accession codes separated with
  #                 a `.`
  
  # error handling
  check.colnames <- all(c("run", "study", "sample") %in% 
                            colnames(conversion.df))
  if (!check.colnames) {
    stop("colnames(conversion.df) must contain 'run', 'study', 'sample'")
  }
  
  # magrittr pipe, just in case
  `%>%` <- dplyr::`%>%`
  
  # filter to only the samples in the supplied vector and then concatenate their
  # study and sample accession codes
  sample.names.df <- conversion.df %>%
    dplyr::filter(`sample` %in% sample.vector) %>%
    dplyr::mutate(recount.name = paste(study, run, sep = "."))
 
  # return a vector of the recount2 names
  return(sample.names.df$recount.name) 
}

Read in data

recount2 data used to train PLIER

plier.file <- file.path("data", "recount2_PLIER_data", 
                        "recount_data_prep_PLIER.RDS")
plier.data <- readRDS(plier.file)

The experiment accession is in the column name of the expression data matrix, as is the run accession.

experiment.accession <- unique(stringr::word(colnames(plier.data$rpkm.cm), 1, 
                                             sep = "[.]"))
head(experiment.accession)
[1] "SRP000599" "SRP001313" "SRP001349" "SRP001462" "SRP001540" "SRP001558"
run.accession <- stringr::word(colnames(plier.data$rpkm.cm), 2, sep = "[.]")
# remove the training data from the workspace, we no longer need it
rm(plier.data)

MetaSRA

metasra.file <- file.path("data", "sample_info", "metasra.v1-4.json")
metasra <- jsonlite::read_json(metasra.file)

The MetaSRA project uses sample identifiers (SRS), whereas recount2 uses study (SRP) and run (SRR) identifiers. We’ll need to convert between the two. We’ll use the SRAdb Bioconductor package to do so.

Identifier conversion

For notebook purposes, I will comment most of this out, as the file downloaded and unzipped by calling SRAdb::getSRAdbFile() is ~35GB. I will save (& commit) the most important part: the data.frame that contains the mapping between accessions.

# sql.file <- getSRAdbFile()
# sql.file <- "SRAmetadb.sqlite"
# sra.con <- dbConnect(SQLite(), sql.file)  
# given the run accession, return the study and sample accesions
# conversion.df <- sraConvert(run.accession, 
#                             out_type = c("study", "sample"), sra.con)
# conversion.file <- file.path("data/sample_info/recount2_srr_srs_srp.tsv")
# readr::write_tsv(conversion.df, conversion.file)
# md5sum SRAmetadb.sqlite

Because we’ve commented the above out for size concerns, we’ll go ahead and read conversion.df back in using the TSV file we’ve saved and committed.

conversion.file <- file.path("data", "sample_info", "recount2_srr_srs_srp.tsv")
conversion.df <- readr::read_tsv(conversion.file)
Parsed with column specification:
cols(
  run = col_character(),
  study = col_character(),
  sample = col_character()
)

Describe recount2

We’ll also use these metadata to split the recount2 data into different training sets based on the biological conditions or sample types.

# only proceed with samples that are in the recount2 compendium that we used
filtered.metasra <- metasra[which(names(metasra) %in% conversion.df$sample)]

Not all of the runs in the subset of the recount2 compendium we used are present in MetaSRA.

length(filtered.metasra) / length(unique(conversion.df$sample))
[1] 0.9940266

But the majority are.

Sample type

The MetaSRA project (Bernstein, Doan, and Dewey. Bioinformatics. 2017.) predicted the “sample type” – e.g., cell line vs. tissue. What’s the breakdown in the compendium we used for training (with no regard for confidence)?

sample.type <- lapply(filtered.metasra, function(x) x$`sample type`)
table(unlist(sample.type))

                         cell line      in vitro differentiated cells induced pluripotent stem cell line 
                             11760                               1760                                545 
                     primary cells                         stem cells                             tissue 
                              4663                               1120                              10272 

The bulk of samples are predicted to be cell lines and tissue samples. Let’s get a list of recount2 sample/column names for the cell line samples and the tissue samples.

cell.line.samples <- names(sample.type[which(sample.type == "cell line")])
cell.line.accessions <- ConvertToRecountSampleName(cell.line.samples,
                                                   conversion.df)
cell.line.file <- file.path("data", "sample_info", 
                            "recount2_cell_line_accessions.tsv")
write.table(cell.line.accessions, file = cell.line.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
tissue.samples <- names(sample.type[which(sample.type == "tissue")])
tissue.accessions <- ConvertToRecountSampleName(tissue.samples,
                                                   conversion.df)
tissue.file <- file.path("data", "sample_info", 
                         "recount2_tissue_accessions.tsv")
write.table(tissue.accessions, file = tissue.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)

Experimental Factor Ontology (EFO)

We’ll use the mapped ontology terms – specifically, the mapped Experimental Factor Ontology terms – provided in MetaSRA to get an idea of what’s in our training set.

We’ve been asked specifically about autoimmune samples and cancer samples, so let’s start there.

Autoimmune disease

# "autoimmune disease"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0005140
autoimmune.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0005140" %in% x$`mapped ontology terms`))
sum(unlist(autoimmune.samples))
[1] 99

There are 99 SLE samples (and 18 controls) in the single SLE whole blood data set in recount2 (SRP062966).

Cancer

# "cancer"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0000311
cancer.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0000311" %in% x$`mapped ontology terms`))
sum(unlist(cancer.samples))
[1] 6760

Let’s write these accession codes to file.

# just use the accession code, which is the name of the list for which the
# element == TRUE
cancer.samples <- names(cancer.samples[which(unlist(cancer.samples))])
cancer.accessions <- 
  ConvertToRecountSampleName(cancer.samples, 
                             conversion.df)
cancer.file <- file.path("data", "sample_info", 
                         "recount2_cancer_accessions.tsv")
write.table(cancer.accessions, file = cancer.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)

What about treated samples?

treatment

From EFO term:

A process in which the act is intended to modify or alter some other material entity

# "treatment"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0000727
treatment.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0000727" %in% x$`mapped ontology terms`))
sum(unlist(treatment.samples))
[1] 4506

Blood samples

We’ll want to train models exclusively on blood samples and on everything-but-blood samples. We’re interested in this because the model learns leukocyte associated latent variables.

We’ll use the Uberon term for this UBERON:0000178

blood.samples <- 
  lapply(filtered.metasra, 
         function(x) any("UBERON:0000178" %in% x$`mapped ontology terms`))
sum(unlist(blood.samples))
[1] 2973

Convert the blood samples and the everything-but-blood samples to the recount2 sample/column names and write to file.

# just the accession codes
blood.samples <- names(blood.samples[which(unlist(blood.samples))])
blood.accessions <- ConvertToRecountSampleName(blood.samples, conversion.df)
blood.file <- file.path("data", "sample_info", 
                        "recount2_blood_accessions.tsv")
write.table(blood.accessions, file = blood.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
not.blood.samples <- setdiff(names(filtered.metasra), blood.samples)
not.blood.accessions <- ConvertToRecountSampleName(not.blood.samples, 
                                                   conversion.df)
not.blood.file <- file.path("data", "sample_info", 
                            "recount2_other_tissues_accessions.tsv")
write.table(not.blood.accessions, file = not.blood.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
---
title: "Describe recount2 compendium"
output: html_notebook
---

**J. Taroni 2018**

In response to comments, we wish to provide a bit more background information 
about the recount2 compendium.
To do so, we'll gather information from the following sources:

* The expression data used to train the model
* [MetaSRA](http://metasra.biostat.wisc.edu/index.html)
* SRAdb (for accession identifier conversion)

## Download MetaSRA data

```{sh}
wget -N -O data/sample_info/metasra.v1-4.json http://metasra.biostat.wisc.edu/static/metasra_versions/v1.4/metasra.v1-4.json
```

## Libraries, functions

```{r}
# BiocInstaller::biocLite("SRAdb")
# library(SRAdb)

# magrittr pipe
`%>%` <- dplyr::`%>%`
```

Custom function for converting between the sample (e.g., `SRS`) accession codes
and the sample names in recount2 which follow this pattern: `SRPxxxxxx.SRRxxxxx`

```{r}
ConvertToRecountSampleName <- function(sample.vector, conversion.df) {
  # Takes a vector of sample accession codes and converts them to sample/column
  # names for use with recount2 expression matrices
  # 
  # Args:
  #   sample.vector: a vector of sample accession codes
  #   conversion.df: a data.frame that provides the mappings between study,
  #                  sample, and run accession codes -- must have "run", 
  #                  "study", and "sample" column names
  # 
  # Returns:
  #   sample.names: a vector of sample/column names for use with recount2 --
  #                 these are the study and run accession codes separated with
  #                 a `.`
  
  # error handling
  check.colnames <- all(c("run", "study", "sample") %in% 
                            colnames(conversion.df))
  if (!check.colnames) {
    stop("colnames(conversion.df) must contain 'run', 'study', 'sample'")
  }
  
  # magrittr pipe, just in case
  `%>%` <- dplyr::`%>%`
  
  # filter to only the samples in the supplied vector and then concatenate their
  # study and sample accession codes
  sample.names.df <- conversion.df %>%
    dplyr::filter(`sample` %in% sample.vector) %>%
    dplyr::mutate(recount.name = paste(study, run, sep = "."))
 
  # return a vector of the recount2 names
  return(sample.names.df$recount.name) 
}
```

## Read in data

### recount2 data used to train PLIER

```{r}
plier.file <- file.path("data", "recount2_PLIER_data", 
                        "recount_data_prep_PLIER.RDS")
plier.data <- readRDS(plier.file)
```

The experiment accession is in the column name of the expression data matrix,
as is the run accession.

```{r}
experiment.accession <- unique(stringr::word(colnames(plier.data$rpkm.cm), 1, 
                                             sep = "[.]"))
head(experiment.accession)
```

```{r}
run.accession <- stringr::word(colnames(plier.data$rpkm.cm), 2, sep = "[.]")
```

```{r}
# remove the training data from the workspace, we no longer need it
rm(plier.data)
```

### MetaSRA

```{r}
metasra.file <- file.path("data", "sample_info", "metasra.v1-4.json")
metasra <- jsonlite::read_json(metasra.file)
```

The MetaSRA project uses _sample_ identifiers (`SRS`), whereas recount2 uses 
_study_ (`SRP`) and _run_ (`SRR`) identifiers.
We'll need to convert between the two. 
We'll use the `SRAdb` Bioconductor package to do so.

#### Identifier conversion

For notebook purposes, I will comment most of this out, as the file downloaded 
and unzipped by calling `SRAdb::getSRAdbFile()` is ~35GB.
I will save (& commit) the most important part: the data.frame that contains
the mapping between accessions.

```{r}
# sql.file <- getSRAdbFile()
```

```{r}
# sql.file <- "SRAmetadb.sqlite"
# sra.con <- dbConnect(SQLite(), sql.file)  
# given the run accession, return the study and sample accesions
# conversion.df <- sraConvert(run.accession, 
#                             out_type = c("study", "sample"), sra.con)
# conversion.file <- file.path("data/sample_info/recount2_srr_srs_srp.tsv")
# readr::write_tsv(conversion.df, conversion.file)
```

```{sh}
# md5sum SRAmetadb.sqlite
```

Because we've commented the above out for size concerns, we'll go ahead and 
read `conversion.df` back in using the TSV file we've saved and committed.

```{r}
conversion.file <- file.path("data", "sample_info", "recount2_srr_srs_srp.tsv")
conversion.df <- readr::read_tsv(conversion.file)
```

## Describe recount2

We'll also use these metadata to split the recount2 data into different training
sets based on the biological conditions or sample types.

```{r}
# only proceed with samples that are in the recount2 compendium that we used
filtered.metasra <- metasra[which(names(metasra) %in% conversion.df$sample)]
```

Not all of the runs in the subset of the recount2 compendium we used are 
present in MetaSRA.

```{r}
length(filtered.metasra) / length(run.accession)
```

But the majority are.

### Sample type

The MetaSRA project ([Bernstein, Doan, and Dewey. _Bioinformatics._ 2017.](https://doi.org/10.1093/bioinformatics/btx334)) 
predicted the "sample type" -- e.g., cell line vs. tissue.
What's the breakdown in the compendium we used for training (with no regard 
for _confidence_)?

```{r}
sample.type <- lapply(filtered.metasra, function(x) x$`sample type`)
table(unlist(sample.type))
```

The bulk of samples are predicted to be cell lines and tissue samples. 
Let's get a list of recount2 sample/column names for the cell line samples and
the tissue samples.

```{r}
cell.line.samples <- names(sample.type[which(sample.type == "cell line")])
cell.line.accessions <- ConvertToRecountSampleName(cell.line.samples,
                                                   conversion.df)
cell.line.file <- file.path("data", "sample_info", 
                            "recount2_cell_line_accessions.tsv")
write.table(cell.line.accessions, file = cell.line.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
```

```{r}
tissue.samples <- names(sample.type[which(sample.type == "tissue")])
tissue.accessions <- ConvertToRecountSampleName(tissue.samples,
                                                   conversion.df)
tissue.file <- file.path("data", "sample_info", 
                         "recount2_tissue_accessions.tsv")
write.table(tissue.accessions, file = tissue.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
```

### Experimental Factor Ontology (EFO)

We'll use the mapped ontology terms -- specifically, the mapped
[Experimental Factor Ontology](https://www.ebi.ac.uk/efo/) terms -- provided in
MetaSRA to get an idea of what's in our training set.

We've been asked specifically about autoimmune samples and cancer samples, so
let's start there.

### Autoimmune disease

```{r}
# "autoimmune disease"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0005140
autoimmune.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0005140" %in% x$`mapped ontology terms`))
sum(unlist(autoimmune.samples))
```

There are 99 SLE samples (and 18 controls) in the single SLE whole blood data 
set in recount2 
([`SRP062966`](http://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP062966)).

### Cancer

```{r}
# "cancer"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0000311
cancer.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0000311" %in% x$`mapped ontology terms`))
sum(unlist(cancer.samples))
```

Let's write these accession codes to file.

```{r}
# just use the accession code, which is the name of the list for which the
# element == TRUE
cancer.samples <- names(cancer.samples[which(unlist(cancer.samples))])
cancer.accessions <- 
  ConvertToRecountSampleName(cancer.samples, 
                             conversion.df)
cancer.file <- file.path("data", "sample_info", 
                         "recount2_cancer_accessions.tsv")
write.table(cancer.accessions, file = cancer.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
```

### What about treated samples?

[`treatment`](https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0000727)

From EFO term:

> A process in which the act is intended to modify or alter some other 
material entity

```{r}
# "treatment"
# https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0000727
treatment.samples <- 
  lapply(filtered.metasra, 
         function(x) any("EFO:0000727" %in% x$`mapped ontology terms`))
sum(unlist(treatment.samples))
```

### Blood samples

We'll want to train models _exclusively_ on blood samples and on 
_everything-but-blood_ samples. 
We're interested in this because the model learns leukocyte associated
latent variables.

We'll use the [Uberon](https://www.ebi.ac.uk/ols/ontologies/uberon) term for this 
[`UBERON:0000178`](https://www.ebi.ac.uk/ols/ontologies/uberon/terms?short_form=UBERON_0000178)

```{r}
blood.samples <- 
  lapply(filtered.metasra, 
         function(x) any("UBERON:0000178" %in% x$`mapped ontology terms`))
sum(unlist(blood.samples))
```

Convert the blood samples and the everything-but-blood samples to the recount2
sample/column names and write to file.

```{r}
# just the accession codes
blood.samples <- names(blood.samples[which(unlist(blood.samples))])
blood.accessions <- ConvertToRecountSampleName(blood.samples, conversion.df)
blood.file <- file.path("data", "sample_info", 
                        "recount2_blood_accessions.tsv")
write.table(blood.accessions, file = blood.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
```

```{r}
not.blood.samples <- setdiff(names(filtered.metasra), blood.samples)
not.blood.accessions <- ConvertToRecountSampleName(not.blood.samples, 
                                                   conversion.df)
not.blood.file <- file.path("data", "sample_info", 
                            "recount2_other_tissues_accessions.tsv")
write.table(not.blood.accessions, file = not.blood.file, sep = "\t", 
            row.names = FALSE, col.names = FALSE, quote = FALSE)
```

