In this assessment, we will examine Epigenomics Roadmap data on the human H1 stem cell line. The Roadmap id number is “E003”. We will use data on build “hg19” of the human genome, “narrowPeak” peak quantification. To make our work simpler, we will restrict our analysis to chromosome 22. Otherwise results are reported on the standard human autosomes (chromosomes 1 to 22).
library(AnnotationHub)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, as.vector, cbind,
## colnames, do.call, duplicated, eval, evalq, Filter, Find, get,
## grep, grepl, intersect, is.unsorted, lapply, lengths, Map,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
## setdiff, sort, table, tapply, union, unique, unlist, unsplitlibrary(Biostrings)## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: IRanges## Loading required package: XVectorlibrary(BSgenome)## Loading required package: GenomeInfoDb## Loading required package: GenomicRanges## Loading required package: rtracklayerlibrary(GenomicRanges)
library(rtracklayer)
library(Rsamtools)
# Load Genomes
source("https://bioconductor.org/biocLite.R")## Bioconductor version 3.2 (BiocInstaller 1.20.3), ?biocLite for help## A new version of Bioconductor is available after installing the most
## recent version of R; see http://bioconductor.org/installbiocLite("BSgenome.Hsapiens.UCSC.hg19")## BioC_mirror: https://bioconductor.org## Using Bioconductor 3.2 (BiocInstaller 1.20.3), R 3.2.3 (2015-12-10).## Installing package(s) 'BSgenome.Hsapiens.UCSC.hg19'##
## The downloaded source packages are in
## '/tmp/RtmpTKeanZ/downloaded_packages'## Old packages: 'boot', 'cluster', 'codetools', 'foreign', 'lattice',
## 'Matrix', 'mgcv', 'nlme', 'survival'library("BSgenome.Hsapiens.UCSC.hg19")
biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")## BioC_mirror: https://bioconductor.org## Using Bioconductor 3.2 (BiocInstaller 1.20.3), R 3.2.3 (2015-12-10).## Installing package(s) 'TxDb.Hsapiens.UCSC.hg19.knownGene'##
## The downloaded source packages are in
## '/tmp/RtmpTKeanZ/downloaded_packages'## Old packages: 'boot', 'cluster', 'codetools', 'foreign', 'lattice',
## 'Matrix', 'mgcv', 'nlme', 'survival'library("TxDb.Hsapiens.UCSC.hg19.knownGene")## Loading required package: GenomicFeatures## Loading required package: AnnotationDbi## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:AnnotationHub':
##
## cachelibrary(BSgenome.Hsapiens.UCSC.hg19)
Hsapiens## Human genome:
## # organism: Homo sapiens (Human)
## # provider: UCSC
## # provider version: hg19
## # release date: Feb. 2009
## # release name: Genome Reference Consortium GRCh37
## # 93 sequences:
## # chr1 chr2 chr3
## # chr4 chr5 chr6
## # chr7 chr8 chr9
## # chr10 chr11 chr12
## # chr13 chr14 chr15
## # ... ... ...
## # chrUn_gl000235 chrUn_gl000236 chrUn_gl000237
## # chrUn_gl000238 chrUn_gl000239 chrUn_gl000240
## # chrUn_gl000241 chrUn_gl000242 chrUn_gl000243
## # chrUn_gl000244 chrUn_gl000245 chrUn_gl000246
## # chrUn_gl000247 chrUn_gl000248 chrUn_gl000249
## # (use 'seqnames()' to see all the sequence names, use the '$' or '[['
## # operator to access a given sequence)# count total bases on chr22
totalBases <- letterFrequency(Hsapiens$chr22, "A") +
letterFrequency(Hsapiens$chr22, "C") +
letterFrequency(Hsapiens$chr22, "G") +
letterFrequency(Hsapiens$chr22, "T")
# count GC bases on chr22
gcBase <- letterFrequency(Hsapiens$chr22, "GC")
#calculate GC ratio on chr22
gcContent <- gcBase/totalBases
gcContent## G|C
## 0.4798807# Obtain H3K27me3 dataset
ah <- AnnotationHub()## snapshotDate(): 2016-08-15H3K27me3_qh <- query(ah, c("H3K27me3", "E003", "narrowPeak"))
H3K27me3_qh## AnnotationHub with 1 record
## # snapshotDate(): 2016-08-15
## # names(): AH29892
## # $dataprovider: BroadInstitute
## # $species: Homo sapiens
## # $rdataclass: GRanges
## # $title: E003-H3K27me3.narrowPeak.gz
## # $description: Narrow ChIP-seq peaks for consolidated epigenomes from ...
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: BED
## # $sourceurl: http://egg2.wustl.edu/roadmap/data/byFileType/peaks/conso...
## # $sourcelastmodifieddate: 2013-10-09
## # $sourcesize: 1926389
## # $tags: EpigenomeRoadMap, peaks, consolidated, narrowPeak, E003,
## # ESC, ESC.H1, H1 Cells
## # retrieve record with 'object[["AH29892"]]'# retrieve data
H3K27me3_data <- H3K27me3_qh[["AH29892"]]
# get genomic ranges on chr22
H3K27me3_data.chr22 <- subset(H3K27me3_data, seqnames == "chr22")
# get DNA seqence on chr22
H3K27me3_data.chr22.seq <- Views(Hsapiens, H3K27me3_data.chr22)
# calculate GC content on chr22
gcContents <- letterFrequency(H3K27me3_data.chr22.seq, "GC", as.prob = T)
meanGcContents <- mean(gcContents)
meanGcContents## [1] 0.528866# values of signalValues
sigV <- mcols(H3K27me3_data.chr22.seq)$signalValue
# calculate correlation
cor(sigV, gcContents)## G|C
## [1,] 0.004467924# Obtain fc.signal file
H3K27me3_fc <- query(ah, c("H3K27me3", "E003", "fc.signal"))
H3K27me3_fc## AnnotationHub with 1 record
## # snapshotDate(): 2016-08-15
## # names(): AH32033
## # $dataprovider: BroadInstitute
## # $species: Homo sapiens
## # $rdataclass: BigWigFile
## # $title: E003-H3K27me3.fc.signal.bigwig
## # $description: Bigwig File containing fold enrichment signal tracks fr...
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: BigWig
## # $sourceurl: http://egg2.wustl.edu/roadmap/data/byFileType/signal/cons...
## # $sourcelastmodifieddate: 2013-10-08
## # $sourcesize: 525643252
## # $tags: EpigenomeRoadMap, signal, consolidated, macs2signal,
## # E003, ESC, ESC.H1, H1 Cells
## # retrieve record with 'object[["AH32033"]]'# retrieve data
H3K27me3_fc.data <- H3K27me3_fc[["AH32033"]]## loading from cache '/home/mcc/.AnnotationHub/37473'# get subset data on chr22
gr.chr22 <- GRanges(seqnames = "chr22",
ranges = IRanges(start =1, end = 51304566))
H3K27me3_fc.rel <- import(H3K27me3_fc.data, which = gr.chr22, as = "Rle")
H3K27me3_fc.rel.chr22 <- H3K27me3_fc.rel$chr22
# view fc.signal data
fc.signal.chr22 <- Views(H3K27me3_fc.rel.chr22,
start = start(H3K27me3_data.chr22),
end = end(H3K27me3_data.chr22))
# calculate mean values
fc.signal.mean <- mean(fc.signal.chr22)
# calculate correlation
cor(fc.signal.mean, sigV)## [1] 0.9149614sum(H3K27me3_fc.rel.chr22 >= 1)## [1] 10914671H3K27me3_fc_E055 <- query(ah, c("H3K27me3", "E055"))
H3K27me3_fc_E055_data <- H3K27me3_fc_E055[[4]]## loading from cache '/home/mcc/.AnnotationHub/37910'# get subset data on chr22
gr.chr22 <- GRanges(seqnames = "chr22",
ranges = IRanges(start =1, end = 51304566))
H3K27me3_fc.rel_E055 <- import(H3K27me3_fc_E055_data, which =gr.chr22, as = "Rle")
H3K27me3_fc.rel_E055.chr22 <- H3K27me3_fc.rel_E055$chr22
# identify region
region_E003 <- slice(H3K27me3_fc.rel.chr22, upper = 0.5)
region_E055 <- slice(H3K27me3_fc.rel_E055.chr22, lower = 2)
region_E003 <- as(region_E003, "IRanges")
region_E055 <- as(region_E055, "IRanges")
inter_region <- intersect(region_E003, region_E055)
sum(width(inter_region))## [1] 1869937# load hg19 CpG dataset
CpGIsland <- query(ah, c("hg19", "CpG Islands"))
CpGIsland_data <- CpGIsland[["AH5086"]]
# get data on chr22
CpGIsland_data.chr22 <- subset(CpGIsland_data, seqnames == "chr22")
CpGIsland_data.chr22.vi <- Views(Hsapiens, CpGIsland_data.chr22)
# calculate observed GC bases
region_length <- width(CpGIsland_data.chr22.vi)
observed_gcBase <- dinucleotideFrequency(CpGIsland_data.chr22.vi)[,7]/region_length
# calculate expected GC bases
freq_C <- letterFrequency(CpGIsland_data.chr22.vi, "C")
freq_G <- letterFrequency(CpGIsland_data.chr22.vi, "G")
expected_gcBase <- (freq_C/region_length)*(freq_G/region_length)
# calculate ration
mean(observed_gcBase/expected_gcBase)## [1] 0.8340929# load hg19 build dataset
library(BSgenome.Hsapiens.UCSC.hg19)
TATA_boxes <- countPattern("TATAAA", Hsapiens$chr22) +
countPattern("TATAAA", reverseComplement(Hsapiens$chr22))
TATA_boxes## [1] 27263library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
gr <- GRanges(seqnames = "chr22",
ranges = IRanges(start = 1, end = 52330658))
gr.trans.chr22 <- subsetByOverlaps(transcripts(txdb),
gr,
ignore.strand = TRUE)
proms <- promoters(gr.trans.chr22,
upstream = 100,
downstream = 900)
cdseq <- subsetByOverlaps(genes(txdb),
gr,
ignore.strand = TRUE)
proms_cds <- findOverlaps(proms, cdseq)
unique(queryHits(proms_cds))## [1] 8 9 10 11 12 13 14 15 16 18 19 20 21
## [14] 22 23 24 25 26 27 28 29 30 31 32 33 34
## [27] 35 36 37 38 39 40 41 42 44 45 46 47 48
## [40] 49 50 51 57 58 59 60 61 62 63 64 66 67
## [53] 68 69 70 71 72 73 74 75 76 77 78 79 80
## [66] 81 82 83 84 85 86 87 88 89 90 91 92 93
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## [92] 107 108 109 110 111 112 114 115 116 117 125 126 127
## [105] 128 129 130 131 132 133 134 135 136 137 138 139 140
## [118] 141 142 143 151 152 153 154 155 157 158 159 160 161
## [131] 162 163 164 165 166 167 168 169 170 171 172 173 174
## [144] 175 179 180 183 184 185 186 187 188 189 195 196 197
## [157] 198 199 200 201 202 203 204 205 206 207 208 209 210
## [170] 211 214 215 216 217 219 223 224 225 226 227 228 229
## [183] 230 231 232 233 234 235 236 237 238 239 240 241 242
## [196] 243 244 245 246 247 248 249 250 251 252 253 254 255
## [209] 256 257 258 259 260 261 262 263 264 265 268 269 270
## [222] 271 272 273 274 275 276 277 278 279 280 281 282 283
## [235] 284 285 286 287 288 289 290 291 292 293 294 295 296
## [248] 297 298 299 300 301 302 303 304 305 306 307 309 310
## [261] 311 312 313 314 315 316 317 318 319 320 321 322 323
## [274] 324 325 326 327 328 329 330 331 332 333 334 335 336
## [287] 337 338 339 340 341 342 343 344 345 346 347 348 349
## [300] 350 351 352 353 355 356 357 358 360 361 362 363 364
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## [326] 378 379 380 381 382 383 384 385 386 387 388 389 390
## [339] 391 392 393 394 395 396 397 398 399 400 401 402 403
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## [521] 578 579 580 581 582 583 584 585 586 587 588 589 590
## [534] 591 592 593 594 595 596 597 598 599 600 601 602 603
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## [560] 617 618 619 620 622 623 624 625 626 627 628 629 630
## [573] 631 632 633 634 635 636 637 638 639 640 641 642 643
## [586] 644 645 646 647 648 650 651 652 653 654 655 656 657
## [599] 658 659 660 661 662 663 664 665 666 667 668 669 670
## [612] 671 672 673 674 675 676 677 678 679 680 681 682 683
## [625] 684 685 686 687 688 689 690 691 692 693 694 695 696
## [638] 697 698 699 700 701 702 703 704 705 706 707 708 709
## [651] 710 711 712 713 714 715 716 717 718 719 720 721 722
## [664] 723 724 725 726 727 728 729 730 731 732 733 734 735
## [677] 736 737 738 740 741 742 743 744 745 746 747 748 749
## [690] 750 751 752 753 754 755 756 757 758 759 760 761 762
## [703] 763 764 765 767 768 769 770 771 773 774 775 776 777
## [716] 778 779 780 781 782 783 784 785 786 787 788 789 790
## [729] 791 792 793 794 795 796 797 798 799 800 801 802 803
## [742] 804 805 806 807 808 809 810 811 812 813 814 815 816
## [755] 817 818 819 820 821 822 823 824 825 826 827 828 829
## [768] 830 831 832 833 834 835 836 837 838 839 840 841 842
## [781] 843 844 845 846 847 848 849 850 851 852 853 854 855
## [794] 856 857 858 859 860 861 862 863 864 865 866 867 868
## [807] 869 870 871 872 873 874 875 876 877 878 879 880 881
## [820] 882 885 886 887 888 889 890 891 892 893 894 895 896
## [833] 897 898 899 900 901 902 903 904 905 906 907 908 909
## [846] 910 911 912 913 914 915 916 917 918 919 920 921 922
## [859] 923 924 925 926 927 928 929 930 931 932 933 934 936
## [872] 937 938 939 940 941 942 943 944 945 946 947 948 949
## [885] 966 967 968 969 970 971 972 974 975 977 978 979 980
## [898] 981 982 983 984 985 986 987 988 989 990 991 992 993
## [911] 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006
## [924] 1007 1008 1009 1010 1011 1012 1013 1014 1017 1018 1019 1020 1021
## [937] 1022 1023 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
## [950] 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048
## [963] 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061
## [976] 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074
## [989] 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087
## [1002] 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1104
## [1015] 1105 1106 1107 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
## [1028] 1121 1130 1131 1132 1133 1134 1135 1138 1139 1140 1141 1142 1143
## [1041] 1144 1145 1146 1147 1148 1149 1150 1151 1152 1154 1155 1156 1157
## [1054] 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170
## [1067] 1171 1172 1173 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## [1080] 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1197 1198
## [1093] 1199 1200 1201 1202 1203 1209 1210 1211 1212 1213 1214 1215 1216
## [1106] 1217 1218 1219 1220 1221 1222 1223 1224 1227 1228 1229 1230 1231
## [1119] 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1245
## [1132] 1246 1247 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
## [1145] 1260 1261 1262 1263 1264 1265 1269 1270 1271 1272 1273 1274 1275
## [1158] 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288
## [1171] 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301
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## [1197] 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
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## [1223] 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353
## [1236] 1354 1355 1356 1357 1358 1359 1360 1363 1364 1365 1366 1367 1368
## [1249] 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381
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## [1275] 1396 1397 1398 1399 1400 1401 1403 1404 1405 1406 1407 1408 1409
## [1288] 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1421 1422 1423
## [1301] 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436
## [1314] 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449
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## [1353] 1477 1478 1479 1480 1481 1482 1483 1484 1534 1535 1536 1537 1538
## [1366] 1539 1540 1541 1542 1543 1544 1545 1546 1549 1550 1551 1552 1553
## [1379] 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566
## [1392] 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579
## [1405] 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592
## [1418] 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606
## [1431] 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619
## [1444] 1620 1621 1623 1624 1625 1626 1627 1628 1629 1630 1631 1634 1635
## [1457] 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
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## [1483] 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1673 1674 1675
## [1496] 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688
## [1509] 1689 1690 1691 1692 1693 1694 1695 1696 1699 1700 1701 1702 1703
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## [1587] 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788
## [1600] 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801
## [1613] 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814
## [1626] 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827
## [1639] 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
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## [1665] 1854 1855 1856 1857 1858 1859 1861 1862 1863 1864 1865 1866 1867
## [1678] 1868count = 0
for (i in unique(queryHits(proms_cds))){
proms_cds_view <- Views(Hsapiens, proms[i])
count = count + vcountPattern("TATAAA", DNAStringSet(proms_cds_view))
}
count## [1] 218library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevels(txdb, force=TRUE) <- c("chr22")
gr <- GRanges(seqnames = "chr22",
ranges = IRanges(start = 1, end = 52330658))
gr.trans.chr22 <- subsetByOverlaps(transcripts(txdb),
gr,
ignore.strand = TRUE)
length(gr.trans.chr22) ## [1] 1868gr.prom <- promoters(gr.trans.chr22,
upstream = 100,
downstream = 900)
tl.chr22 <- transcriptLengths(txdb,
with.cds_len = TRUE) #rtn df
tl.chr22 <- tl.chr22[tl.chr22$cds_len > 0,]
trans.eval <- gr.prom[mcols(gr.prom)$tx_id %in% tl.chr22$tx_id]
sum(coverage(trans.eval) > 1)## chr22
## 309991sessionInfo()## R version 3.2.3 (2015-12-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [2] GenomicFeatures_1.22.13
## [3] AnnotationDbi_1.32.3
## [4] Biobase_2.30.0
## [5] BSgenome.Hsapiens.UCSC.hg19_1.4.0
## [6] BiocInstaller_1.20.3
## [7] Rsamtools_1.22.0
## [8] BSgenome_1.38.0
## [9] rtracklayer_1.30.4
## [10] GenomicRanges_1.22.4
## [11] GenomeInfoDb_1.6.3
## [12] Biostrings_2.38.4
## [13] XVector_0.10.0
## [14] IRanges_2.4.8
## [15] S4Vectors_0.8.11
## [16] AnnotationHub_2.2.5
## [17] BiocGenerics_0.16.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.7 futile.logger_1.4.3
## [3] formatR_1.4 futile.options_1.0.0
## [5] bitops_1.0-6 tools_3.2.3
## [7] zlibbioc_1.16.0 biomaRt_2.26.1
## [9] digest_0.6.10 evaluate_0.9
## [11] RSQLite_1.0.0 shiny_0.14.1
## [13] DBI_0.5-1 curl_2.1
## [15] yaml_2.1.13 httr_1.2.1
## [17] stringr_1.1.0 knitr_1.14
## [19] R6_2.2.0 BiocParallel_1.4.3
## [21] XML_3.98-1.4 rmarkdown_1.0
## [23] lambda.r_1.1.9 magrittr_1.5
## [25] GenomicAlignments_1.6.3 htmltools_0.3.5
## [27] SummarizedExperiment_1.0.2 mime_0.5
## [29] interactiveDisplayBase_1.8.0 xtable_1.8-2
## [31] httpuv_1.3.3 stringi_1.1.2
## [33] RCurl_1.95-4.8