Alternative polyadenylation (APA) is one of the important post-transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites from RNAseq data. It leverages cleanUpdTSeq to fine tune identified APA sites.
Alternative polyadenylation (APA) is one of the most important post-transcriptional regulation mechanisms which occurs in most human genes. APA in a gene can result in altered expression of the gene, which can lead pathological effect to the cells, such as uncontrolled cell cycle and growth. However, there are only limited ways to identify and quantify APA in genes, and most of them suffers from complicated process for library construction and requires large amount of RNAs.
RNA-seq has become one of the most commonly used methods for quantifying genome-wide gene expression. There are massive RNA-seq datasets available publicly such as GEO and TCGA. For this reason, we develop the InPAS algorithm for identifying APA from conventional RNA-seq data.
The workflow for InPAS is:
To use InPAS, BSgenome and TxDb object have to be loaded before run.
library(InPAS)
library(BSgenome.Mmusculus.UCSC.mm10)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
path <- file.path(find.package("InPAS"), "extdata")Users can prepare annotaiton by utr3Annotation with a TxDb and org annotation. The 3UTR annotation prepared by utr3Annotation includes the gaps to next annotation region.
library(org.Hs.eg.db)
samplefile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package="GenomicFeatures")
txdb <- loadDb(samplefile)
utr3.sample.anno <- utr3Annotation(txdb=txdb,
orgDbSYMBOL="org.Hs.egSYMBOL")
utr3.sample.anno## GRanges object with 155 ranges and 7 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## uc001bum.2_5|IQCC|utr3 chr1 [ 32673684, 32674288] + |
## uc001fbq.3_3|S100A9|utr3 chr1 [153333315, 153333503] + |
## uc001gde.2_2|LRRC52|utr3 chr1 [165533062, 165533185] + |
## uc001hfg.3_15|PFKFB2|utr3 chr1 [207245717, 207251162] + |
## uc001hfh.3_15|PFKFB2|utr3 chr1 [207252365, 207254368] + |
## ... ... ... ... .
## uc004dsv.3_19|PHF8|CDS chrX [ 53964414, 53964492] - |
## uc004dsx.3_15|PHF8|CDS chrX [ 53969797, 53969835] - |
## uc004ehz.1_5|ARMCX3|CDS chrX [100879970, 100881109] + |
## uc004elw.3_6|FAM199X|CDS chrX [103434289, 103434459] + |
## uc004fmj.1_10|GAB3|CDS chrX [153906455, 153906571] - |
## feature annotatedProximalCP exon
## <character> <character> <character>
## uc001bum.2_5|IQCC|utr3 utr3 unknown uc001bum.2_5
## uc001fbq.3_3|S100A9|utr3 utr3 unknown uc001fbq.3_3
## uc001gde.2_2|LRRC52|utr3 utr3 unknown uc001gde.2_2
## uc001hfg.3_15|PFKFB2|utr3 utr3 unknown uc001hfg.3_15
## uc001hfh.3_15|PFKFB2|utr3 utr3 unknown uc001hfh.3_15
## ... ... ... ...
## uc004dsv.3_19|PHF8|CDS CDS unknown uc004dsv.3_19
## uc004dsx.3_15|PHF8|CDS CDS unknown uc004dsx.3_15
## uc004ehz.1_5|ARMCX3|CDS CDS unknown uc004ehz.1_5
## uc004elw.3_6|FAM199X|CDS CDS unknown uc004elw.3_6
## uc004fmj.1_10|GAB3|CDS CDS unknown uc004fmj.1_10
## transcript gene symbol truncated
## <character> <character> <character> <logical>
## uc001bum.2_5|IQCC|utr3 uc001bum.2 55721 IQCC FALSE
## uc001fbq.3_3|S100A9|utr3 uc001fbq.3 6280 S100A9 FALSE
## uc001gde.2_2|LRRC52|utr3 uc001gde.2 440699 LRRC52 FALSE
## uc001hfg.3_15|PFKFB2|utr3 uc001hfg.3 5208 PFKFB2 FALSE
## uc001hfh.3_15|PFKFB2|utr3 uc001hfh.3 5208 PFKFB2 FALSE
## ... ... ... ... ...
## uc004dsv.3_19|PHF8|CDS uc004dsv.3 23133 PHF8 FALSE
## uc004dsx.3_15|PHF8|CDS uc004dsx.3 23133 PHF8 FALSE
## uc004ehz.1_5|ARMCX3|CDS uc004ehz.1 51566 ARMCX3 FALSE
## uc004elw.3_6|FAM199X|CDS uc004elw.3 139231 FAM199X FALSE
## uc004fmj.1_10|GAB3|CDS uc004fmj.1 139716 GAB3 FALSE
## -------
## seqinfo: 27 sequences from hg19 genome; no seqlengthsUsers can load mm10 and hg19 annotation from pre-prepared data. Here we loaded the prepared mm10 3UTR annotation file.
##step1 annotation
# load from dataset
data(utr3.mm10)The coverage is calculated from BEDGraph file. The RNA-seq BAM files could be converted to BEDGraph files by bedtools genomecov tool (parameter: -bg -split). PWM and a classifier of polyA signal can be used for adjusting CP sites prediction.
load(file.path(path, "polyA.rds"))
library(cleanUpdTSeq)
data(classifier)
bedgraphs <- c(file.path(path, "Baf3.extract.bedgraph"),
file.path(path, "UM15.extract.bedgraph"))
hugeData <- FALSE
##step1 Calculate coverage
coverage <- coverageFromBedGraph(bedgraphs,
tags=c("Baf3", "UM15"),
genome=BSgenome.Mmusculus.UCSC.mm10,
hugeData=hugeData)
## we hope the coverage rate of should be greater than 0.75 in the expressed gene.
## which is used because the demo data is a subset of genome.
coverageRate(coverage=coverage,
txdb=TxDb.Mmusculus.UCSC.mm10.knownGene,
genome=BSgenome.Mmusculus.UCSC.mm10,
which = GRanges("chr6", ranges=IRanges(98013000, 140678000)))## strand information will be ignored.## gene.coverage.rate expressed.gene.coverage.rate UTR3.coverage.rate
## Baf3 0.01273315 0.6582754 0.02317380
## UM15 0.01280700 0.6621169 0.02337027
## UTR3.expressed.gene.subset.coverage.rate
## Baf3 0.8102631
## UM15 0.8171327##step2 Predict cleavage sites
CPs <- CPsites(coverage=coverage,
genome=BSgenome.Mmusculus.UCSC.mm10,
utr3=utr3.mm10,
search_point_START=200,
cutEnd=.2,
long_coverage_threshold=3,
background="10K",
txdb=TxDb.Mmusculus.UCSC.mm10.knownGene,
PolyA_PWM=pwm,
classifier=classifier,
shift_range=50,
step=10)
head(CPs)## GRanges object with 4 ranges and 12 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## uc009daz.2_10|Mitf|utr3 chr6 [ 98018176, 98021358] + |
## uc009dhz.2_19|Atg7|utr3 chr6 [114859343, 114860614] + |
## uc009dmb.2_4|Lrtm2|utr3 chr6 [119315133, 119317055] - |
## uc009eet.1_3|BC035044|utr3 chr6 [128848044, 128850081] - |
## annotatedProximalCP exon transcript
## <character> <character> <character>
## uc009daz.2_10|Mitf|utr3 unknown uc009daz.2_10 uc009daz.2
## uc009dhz.2_19|Atg7|utr3 unknown uc009dhz.2_19 uc009dhz.2
## uc009dmb.2_4|Lrtm2|utr3 unknown uc009dmb.2_4 uc009dmb.2
## uc009eet.1_3|BC035044|utr3 unknown uc009eet.1_3 uc009eet.1
## gene symbol truncated fit_value
## <character> <character> <logical> <numeric>
## uc009daz.2_10|Mitf|utr3 17342 Mitf FALSE 12594.6737
## uc009dhz.2_19|Atg7|utr3 74244 Atg7 FALSE 27383.4125
## uc009dmb.2_4|Lrtm2|utr3 211187 Lrtm2 FALSE 168.5509
## uc009eet.1_3|BC035044|utr3 232406 BC035044 FALSE 128.8275
## Predicted_Proximal_APA Predicted_Distal_APA
## <numeric> <numeric>
## uc009daz.2_10|Mitf|utr3 98018978 98021358
## uc009dhz.2_19|Atg7|utr3 114859674 114862071
## uc009dmb.2_4|Lrtm2|utr3 119316541 119315133
## uc009eet.1_3|BC035044|utr3 128849128 128846244
## type utr3start utr3end
## <character> <numeric> <numeric>
## uc009daz.2_10|Mitf|utr3 novel proximal 98018276 98021358
## uc009dhz.2_19|Atg7|utr3 novel distal 114859443 114860614
## uc009dmb.2_4|Lrtm2|utr3 novel proximal 119316955 119315133
## uc009eet.1_3|BC035044|utr3 novel distal 128849981 128848044
## -------
## seqinfo: 42 sequences from mm10 genome; no seqlengths##step3 Estimate 3UTR usage
res <- testUsage(CPsites=CPs,
coverage=coverage,
genome=BSgenome.Mmusculus.UCSC.mm10,
utr3=utr3.mm10,
method="fisher.exact",
gp1="Baf3", gp2="UM15")
##step4 view the results
as(res, "GRanges") ## GRanges object with 4 ranges and 27 metadata columns:
## seqnames ranges strand | annotatedProximalCP
## <Rle> <IRanges> <Rle> | <character>
## uc009daz.2 chr6 [ 98018176, 98021358] + | unknown
## uc009dhz.2 chr6 [114859343, 114862071] + | unknown
## uc009dmb.2 chr6 [119315133, 119317055] - | unknown
## uc009eet.1 chr6 [128846244, 128850081] - | unknown
## transcript gene symbol truncated fit_value
## <character> <character> <character> <logical> <numeric>
## uc009daz.2 uc009daz.2 17342 Mitf FALSE 12594.6737
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 27383.4125
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 168.5509
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 128.8275
## Predicted_Proximal_APA Predicted_Distal_APA type
## <numeric> <numeric> <character>
## uc009daz.2 98018978 98021358 novel proximal
## uc009dhz.2 114859674 114862071 novel distal
## uc009dmb.2 119316541 119315133 novel proximal
## uc009eet.1 128849128 128846244 novel distal
## utr3start utr3end Baf3_short.form.usage
## <numeric> <numeric> <numeric>
## uc009daz.2 98018276 98021358 33.531338
## uc009dhz.2 114859443 114860614 520.914790
## uc009dmb.2 119316955 119315133 8.857865
## uc009eet.1 128849981 128848044 22.671784
## UM15_short.form.usage Baf3_long.form.usage
## <numeric> <numeric>
## uc009daz.2 1.0521497 282.824024
## uc009dhz.2 172.1523492 208.024604
## uc009dmb.2 49.5356160 8.535131
## uc009eet.1 0.7334097 7.876603
## UM15_long.form.usage Baf3_PDUI UM15_PDUI short.mean.gp1
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 189.14112 0.8940074 0.9944680 33.531338
## uc009dhz.2 456.54462 0.2853798 0.7261760 520.914790
## uc009dmb.2 70.81263 0.4907223 0.5883977 8.857865
## uc009eet.1 21.96014 0.2578402 0.9676820 22.671784
## long.mean.gp1 short.mean.gp2 long.mean.gp2 PDUI.gp1
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 282.824024 1.0521497 189.14112 0.8940074
## uc009dhz.2 208.024604 172.1523492 456.54462 0.2853798
## uc009dmb.2 8.535131 49.5356160 70.81263 0.4907223
## uc009eet.1 7.876603 0.7334097 21.96014 0.2578402
## PDUI.gp2 dPDUI logFC P.Value adj.P.Val
## <numeric> <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 0.9944680 -0.10046063 -0.1536382 1.586980e-06 2.115973e-06
## uc009dhz.2 0.7261760 -0.44079612 -1.3474358 1.709476e-60 6.837904e-60
## uc009dmb.2 0.5883977 -0.09767539 -0.2618847 5.930309e-01 5.930309e-01
## uc009eet.1 0.9676820 -0.70984179 -1.9080557 2.632879e-08 5.265757e-08
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsfilterRes(res, gp1="Baf3", gp2="UM15",
background_coverage_threshold=3,
adj.P.Val_cutoff=0.05,
dPDUI_cutoff=0.3,
PDUI_logFC_cutoff=0.59)## GRanges object with 4 ranges and 28 metadata columns:
## seqnames ranges strand | annotatedProximalCP
## <Rle> <IRanges> <Rle> | <character>
## uc009daz.2 chr6 [ 98018176, 98021358] + | unknown
## uc009dhz.2 chr6 [114859343, 114862071] + | unknown
## uc009dmb.2 chr6 [119315133, 119317055] - | unknown
## uc009eet.1 chr6 [128846244, 128850081] - | unknown
## transcript gene symbol truncated fit_value
## <character> <character> <character> <logical> <numeric>
## uc009daz.2 uc009daz.2 17342 Mitf FALSE 12594.6737
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 27383.4125
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 168.5509
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 128.8275
## Predicted_Proximal_APA Predicted_Distal_APA type
## <numeric> <numeric> <character>
## uc009daz.2 98018978 98021358 novel proximal
## uc009dhz.2 114859674 114862071 novel distal
## uc009dmb.2 119316541 119315133 novel proximal
## uc009eet.1 128849128 128846244 novel distal
## utr3start utr3end Baf3_short.form.usage
## <numeric> <numeric> <numeric>
## uc009daz.2 98018276 98021358 33.531338
## uc009dhz.2 114859443 114860614 520.914790
## uc009dmb.2 119316955 119315133 8.857865
## uc009eet.1 128849981 128848044 22.671784
## UM15_short.form.usage Baf3_long.form.usage
## <numeric> <numeric>
## uc009daz.2 1.0521497 282.824024
## uc009dhz.2 172.1523492 208.024604
## uc009dmb.2 49.5356160 8.535131
## uc009eet.1 0.7334097 7.876603
## UM15_long.form.usage Baf3_PDUI UM15_PDUI short.mean.gp1
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 189.14112 0.8940074 0.9944680 33.531338
## uc009dhz.2 456.54462 0.2853798 0.7261760 520.914790
## uc009dmb.2 70.81263 0.4907223 0.5883977 8.857865
## uc009eet.1 21.96014 0.2578402 0.9676820 22.671784
## long.mean.gp1 short.mean.gp2 long.mean.gp2 PDUI.gp1
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 282.824024 1.0521497 189.14112 0.8940074
## uc009dhz.2 208.024604 172.1523492 456.54462 0.2853798
## uc009dmb.2 8.535131 49.5356160 70.81263 0.4907223
## uc009eet.1 7.876603 0.7334097 21.96014 0.2578402
## PDUI.gp2 dPDUI logFC P.Value adj.P.Val
## <numeric> <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 0.9944680 -0.10046063 -0.1536382 1.586980e-06 2.115973e-06
## uc009dhz.2 0.7261760 -0.44079612 -1.3474358 1.709476e-60 6.837904e-60
## uc009dmb.2 0.5883977 -0.09767539 -0.2618847 5.930309e-01 5.930309e-01
## uc009eet.1 0.9676820 -0.70984179 -1.9080557 2.632879e-08 5.265757e-08
## PASS
## <logical>
## uc009daz.2 FALSE
## uc009dhz.2 TRUE
## uc009dmb.2 FALSE
## uc009eet.1 TRUE
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe process described above can be done in one step.
if(interactive()){
res <- inPAS(bedgraphs=bedgraphs, tags=c("Baf3", "UM15"),
genome=BSgenome.Mmusculus.UCSC.mm10,
utr3=utr3.mm10, gp1="Baf3", gp2="UM15",
txdb=TxDb.Mmusculus.UCSC.mm10.knownGene,
search_point_START=200,
short_coverage_threshold=15,
long_coverage_threshold=3,
cutStart=0, cutEnd=.2,
hugeData=FALSE,
shift_range=50,
PolyA_PWM=pwm, classifier=classifier,
method="fisher.exact",
adj.P.Val_cutoff=0.05,
dPDUI_cutoff=0.3,
PDUI_logFC_cutoff=0.59)
}InPAS can handle single group data.
inPAS(bedgraphs=bedgraphs[1], tags=c("Baf3"),
genome=BSgenome.Mmusculus.UCSC.mm10,
utr3=utr3.mm10, gp1="Baf3", gp2=NULL,
txdb=TxDb.Mmusculus.UCSC.mm10.knownGene,
search_point_START=200,
short_coverage_threshold=15,
long_coverage_threshold=3,
cutStart=0, cutEnd=.2,
hugeData=FALSE,
PolyA_PWM=pwm, classifier=classifier,
method="singleSample",
adj.P.Val_cutoff=0.05,
step=10)## converged at iteration 1 with logLik: -1835.501
## converged at iteration 5 with logLik: -838.8306
## converged at iteration 1 with logLik: -1496.738
## converged at iteration 13 with logLik: -724.6964
## converged at iteration 1 with logLik: -997.3022
## converged at iteration 8 with logLik: -555.5656
## converged at iteration 1 with logLik: -188.938
## converged at iteration 22 with logLik: -152.6663
## converged at iteration 1 with logLik: -462.3804
## converged at iteration 11 with logLik: -214.1651## dPDUI is calculated by gp2 - gp1.## GRanges object with 6 ranges and 22 metadata columns:
## seqnames ranges strand | annotatedProximalCP
## <Rle> <IRanges> <Rle> | <character>
## uc009daz.2 chr6 [ 98018176, 98021358] + | unknown
## uc009dhz.2 chr6 [114859343, 114862075] + | unknown
## uc009die.2 chr6 [114860617, 114862164] - | unknown
## uc009dmb.2 chr6 [119315133, 119317055] - | unknown
## uc009eet.1 chr6 [128847265, 128850081] - | unknown
## uc009eol.1 chr6 [140651362, 140651622] + | unknown
## transcript gene symbol truncated fit_value
## <character> <character> <character> <logical> <numeric>
## uc009daz.2 uc009daz.2 17342 Mitf FALSE 17843.70792
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 7630.49809
## uc009die.2 uc009die.2 232334 Vgll4 FALSE 10704.69891
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 18.63507
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 227.54183
## uc009eol.1 uc009eol.1 11569 Aebp2 TRUE 204539.69842
## Predicted_Proximal_APA Predicted_Distal_APA type
## <numeric> <numeric> <character>
## uc009daz.2 98019022 98021358 novel proximal
## uc009dhz.2 114860283 114862075 novel distal
## uc009die.2 114861446 114860617 novel distal
## uc009dmb.2 119316683 119315133 novel proximal
## uc009eet.1 128848843 128847265 novel distal
## uc009eol.1 140651566 140651622 novel proximal
## utr3start utr3end data2 Baf3_short.form.usage
## <numeric> <numeric> <list> <numeric>
## uc009daz.2 98018176 98021358 473,460,457,... 29.660257
## uc009dhz.2 114859343 114860614 184,184,184,... 3.725449
## uc009die.2 114862164 114862092 76,76,76,... 121.888138
## uc009dmb.2 119317055 119315133 11,12,12,... 9.226098
## uc009eet.1 128850081 128848044 22,22,22,... 22.044952
## uc009eol.1 140651362 140651622 1408,1412,1384,... 1065.980134
## Baf3_long.form.usage Baf3_PDUI short.mean
## <numeric> <numeric> <list>
## uc009daz.2 283.389388 0.9052538 29.6602572856635
## uc009dhz.2 209.978806 0.9825673 3.7254488495449
## uc009die.2 174.122892 0.5882311 121.8881378455
## uc009dmb.2 9.117988 0.4970533 9.22609762692124
## uc009eet.1 10.327422 0.3190196 22.0449523788087
## uc009eol.1 221.456140 0.1720133 1065.98013415893
## long.mean PDUI P.Value adj.P.Val
## <list> <list> <list> <list>
## uc009daz.2 283.389388104407 0.905253822444981 1 1
## uc009dhz.2 209.978806469604 0.982567268751943 1 1
## uc009die.2 174.122891566265 0.588231093659866 1 1
## uc009dmb.2 9.11798839458414 0.497053294663731 1 1
## uc009eet.1 10.3274224192527 0.319019611124456 1 1
## uc009eol.1 221.456140350877 0.172013283092553 1 1
## dPDUI PASS
## <numeric> <logical>
## uc009daz.2 <NA> FALSE
## uc009dhz.2 <NA> FALSE
## uc009die.2 <NA> FALSE
## uc009dmb.2 <NA> FALSE
## uc009eet.1 <NA> FALSE
## uc009eol.1 <NA> FALSE
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthssessionInfo()R version 3.4.0 (2017-04-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.2 LTS
Matrix products: default BLAS: /home/biocbuild/bbs-3.5-bioc/R/lib/libRblas.so LAPACK: /home/biocbuild/bbs-3.5-bioc/R/lib/libRlapack.so
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
[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] cleanUpdTSeq_1.14.0
[2] e1071_1.6-8
[3] seqinr_3.3-6
[4] BSgenome.Drerio.UCSC.danRer7_1.4.0
[5] org.Hs.eg.db_3.4.1
[6] TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.0 [7] BSgenome.Mmusculus.UCSC.mm10_1.4.0
[8] BSgenome_1.44.0
[9] rtracklayer_1.36.0
[10] Biostrings_2.44.0
[11] XVector_0.16.0
[12] InPAS_1.8.0
[13] GenomicFeatures_1.28.0
[14] AnnotationDbi_1.38.0
[15] GenomicRanges_1.28.0
[16] GenomeInfoDb_1.12.0
[17] IRanges_2.10.0
[18] S4Vectors_0.14.0
[19] Biobase_2.36.0
[20] BiocGenerics_0.22.0
[21] BiocStyle_2.4.0
loaded via a namespace (and not attached): [1] ProtGenerics_1.8.0 bitops_1.0-6
[3] matrixStats_0.52.2 RColorBrewer_1.1-2
[5] httr_1.2.1 rprojroot_1.2
[7] tools_3.4.0 backports_1.0.5
[9] R6_2.2.0 rpart_4.1-11
[11] Hmisc_4.0-2 DBI_0.6-1
[13] lazyeval_0.2.0 Gviz_1.20.0
[15] colorspace_1.3-2 ade4_1.7-6
[17] nnet_7.3-12 gridExtra_2.2.1
[19] compiler_3.4.0 preprocessCore_1.38.0
[21] htmlTable_1.9 DelayedArray_0.2.0
[23] scales_0.4.1 checkmate_1.8.2
[25] stringr_1.2.0 digest_0.6.12
[27] Rsamtools_1.28.0 foreign_0.8-67
[29] rmarkdown_1.4 base64enc_0.1-3
[31] dichromat_2.0-0 htmltools_0.3.5
[33] ensembldb_2.0.0 limma_3.32.0
[35] htmlwidgets_0.8 RSQLite_1.1-2
[37] BiocInstaller_1.26.0 shiny_1.0.2
[39] BiocParallel_1.10.0 acepack_1.4.1
[41] VariantAnnotation_1.22.0 RCurl_1.95-4.8
[43] magrittr_1.5 GenomeInfoDbData_0.99.0
[45] Formula_1.2-1 Matrix_1.2-9
[47] Rcpp_0.12.10 munsell_0.4.3
[49] stringi_1.1.5 yaml_2.1.14
[51] MASS_7.3-47 SummarizedExperiment_1.6.0
[53] zlibbioc_1.22.0 plyr_1.8.4
[55] AnnotationHub_2.8.0 grid_3.4.0
[57] lattice_0.20-35 splines_3.4.0
[59] knitr_1.15.1 biomaRt_2.32.0
[61] XML_3.98-1.6 evaluate_0.10
[63] biovizBase_1.24.0 latticeExtra_0.6-28
[65] data.table_1.10.4 httpuv_1.3.3
[67] gtable_0.2.0 ggplot2_2.2.1
[69] mime_0.5 xtable_1.8-2
[71] depmixS4_1.3-3 AnnotationFilter_1.0.0
[73] Rsolnp_1.16 class_7.3-14
[75] survival_2.41-3 truncnorm_1.0-7
[77] tibble_1.3.0 GenomicAlignments_1.12.0
[79] memoise_1.1.0 cluster_2.0.6
[81] interactiveDisplayBase_1.14.0