InPAS 1.20.0
Alternative polyadenylation (APA) is one of the most important post-transcriptional regulation mechanisms which is prevalent in human genes. Like alternative splicing, APA can create proteome diversity. In addition, it defines 3’ UTR and results in altered expression of the gene. It is a tightly controlled process and mis-regulation of APA can lead to pathological effects to the cells, such as uncontrolled cell cycle and growth. Although several high throughput sequencing methods have been developed, there are still limited data available.
RNA-seq has become one of the most commonly used methods for quantifying genome-wide gene expression. There are massive RNA-seq datasets available in public databases 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:
First, load the required libraries InPAS, species specific BSgenome, and TxDb as follows.
library(InPAS)
library(BSgenome.Mmusculus.UCSC.mm10)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
path <- file.path(find.package("InPAS"), "extdata")Next, prepare annotation using the function utr3Annotation with a TxDb and org annotation.
Please note that the 3’ UTR annotation prepared by utr3Annotation
includes the gaps to the next annotated 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 | feature
## <Rle> <IRanges> <Rle> | <character>
## uc001bum.2_5|IQCC|utr3 chr1 32673684-32674288 + | utr3
## uc001fbq.3_3|S100A9|utr3 chr1 153333315-153333503 + | utr3
## uc001gde.2_2|LRRC52|utr3 chr1 165533062-165533185 + | utr3
## uc001hfg.3_15|PFKFB2|utr3 chr1 207245717-207251162 + | utr3
## uc001hfh.3_15|PFKFB2|utr3 chr1 207252365-207254368 + | utr3
## ... ... ... ... . ...
## uc004dsv.3_19|PHF8|CDS chrX 53964414-53964492 - | CDS
## uc004dsx.3_15|PHF8|CDS chrX 53969797-53969835 - | CDS
## uc004ehz.1_5|ARMCX3|CDS chrX 100879970-100881109 + | CDS
## uc004elw.3_6|FAM199X|CDS chrX 103434289-103434459 + | CDS
## uc004fmj.1_10|GAB3|CDS chrX 153906455-153906571 - | CDS
## annotatedProximalCP exon transcript
## <character> <character> <character>
## uc001bum.2_5|IQCC|utr3 unknown uc001bum.2_5 uc001bum.2
## uc001fbq.3_3|S100A9|utr3 unknown uc001fbq.3_3 uc001fbq.3
## uc001gde.2_2|LRRC52|utr3 unknown uc001gde.2_2 uc001gde.2
## uc001hfg.3_15|PFKFB2|utr3 unknown uc001hfg.3_15 uc001hfg.3
## uc001hfh.3_15|PFKFB2|utr3 unknown uc001hfh.3_15 uc001hfh.3
## ... ... ... ...
## uc004dsv.3_19|PHF8|CDS unknown uc004dsv.3_19 uc004dsv.3
## uc004dsx.3_15|PHF8|CDS unknown uc004dsx.3_15 uc004dsx.3
## uc004ehz.1_5|ARMCX3|CDS unknown uc004ehz.1_5 uc004ehz.1
## uc004elw.3_6|FAM199X|CDS unknown uc004elw.3_6 uc004elw.3
## uc004fmj.1_10|GAB3|CDS unknown uc004fmj.1_10 uc004fmj.1
## gene symbol truncated
## <character> <character> <logical>
## uc001bum.2_5|IQCC|utr3 55721 IQCC FALSE
## uc001fbq.3_3|S100A9|utr3 6280 S100A9 FALSE
## uc001gde.2_2|LRRC52|utr3 440699 LRRC52 FALSE
## uc001hfg.3_15|PFKFB2|utr3 5208 PFKFB2 FALSE
## uc001hfh.3_15|PFKFB2|utr3 5208 PFKFB2 FALSE
## ... ... ... ...
## uc004dsv.3_19|PHF8|CDS 23133 PHF8 FALSE
## uc004dsx.3_15|PHF8|CDS 23133 PHF8 FALSE
## uc004ehz.1_5|ARMCX3|CDS 51566 ARMCX3 FALSE
## uc004elw.3_6|FAM199X|CDS 139231 FAM199X FALSE
## uc004fmj.1_10|GAB3|CDS 139716 GAB3 FALSE
## -------
## seqinfo: 27 sequences from hg19 genome; no seqlengthsUsers can also directly load the 3’ UTR annotation included in this package for mm10 and hg19. Here we show how to load the pre-built mm10 3’ UTR annotation file.
##step1 annotation
# load from dataset
data(utr3.mm10)For coverage calculation, alignment files in BEDGraph format are required, which can be generated from BAM files using the genomecov tool in bedtools with parameter: -bg -split. Potential pA sites identified from the coverage data can be filtered/adjusted using the classifier provided by cleanUpdTseq. The following scripts illustrate the function calls needed to perform the complete analysis using InPAS.
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 0.01031381 0.6300834 0.01830495
## UM15 0.01020966 0.6236834 0.01846496
## UTR3.expressed.gene.subset.coverage.rate
## Baf3 0.8082201
## UM15 0.8152853##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 12594.674
## uc009dhz.2_19|Atg7|utr3 74244 Atg7 FALSE 27383.413
## uc009dmb.2_4|Lrtm2|utr3 211187 Lrtm2 FALSE 168.551
## uc009eet.1_3|BC035044|utr3 232406 BC035044 FALSE 123.974
## 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 128849177 128846744
## 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 128846744-128850081 - | unknown
## transcript gene symbol truncated fit_value
## <character> <character> <character> <logical> <numeric>
## uc009daz.2 uc009daz.2 17342 Mitf FALSE 12594.674
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 27383.413
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 168.551
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 123.974
## 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 128849177 128846744 novel distal
## utr3start utr3end Baf3_short.form.usage UM15_short.form.usage
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 98018276 98021358 33.53134 1.05215
## uc009dhz.2 114859443 114860614 520.91479 172.15235
## uc009dmb.2 119316955 119315133 8.85786 49.53562
## uc009eet.1 128849981 128848044 21.64617 0.00000
## Baf3_long.form.usage UM15_long.form.usage Baf3_PDUI UM15_PDUI
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 282.82402 189.1411 0.894007 0.994468
## uc009dhz.2 208.02460 456.5446 0.285380 0.726176
## uc009dmb.2 8.53513 70.8126 0.490722 0.588398
## uc009eet.1 8.90222 23.2913 0.291414 1.000000
## short.mean.gp1 long.mean.gp1 short.mean.gp2 long.mean.gp2
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 33.53134 282.82402 1.05215 189.1411
## uc009dhz.2 520.91479 208.02460 172.15235 456.5446
## uc009dmb.2 8.85786 8.53513 49.53562 70.8126
## uc009eet.1 21.64617 8.90222 0.00000 23.2913
## PDUI.gp1 PDUI.gp2 dPDUI logFC P.Value adj.P.Val
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 0.894007 0.994468 -0.1004606 -0.153638 1.58698e-06 2.11597e-06
## uc009dhz.2 0.285380 0.726176 -0.4407961 -1.347436 1.70948e-60 6.83790e-60
## uc009dmb.2 0.490722 0.588398 -0.0976754 -0.261885 5.93031e-01 5.93031e-01
## uc009eet.1 0.291414 1.000000 -0.7085863 -1.778859 4.13501e-08 8.27003e-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 128846744-128850081 - | unknown
## transcript gene symbol truncated fit_value
## <character> <character> <character> <logical> <numeric>
## uc009daz.2 uc009daz.2 17342 Mitf FALSE 12594.674
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 27383.413
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 168.551
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 123.974
## 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 128849177 128846744 novel distal
## utr3start utr3end Baf3_short.form.usage UM15_short.form.usage
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 98018276 98021358 33.53134 1.05215
## uc009dhz.2 114859443 114860614 520.91479 172.15235
## uc009dmb.2 119316955 119315133 8.85786 49.53562
## uc009eet.1 128849981 128848044 21.64617 0.00000
## Baf3_long.form.usage UM15_long.form.usage Baf3_PDUI UM15_PDUI
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 282.82402 189.1411 0.894007 0.994468
## uc009dhz.2 208.02460 456.5446 0.285380 0.726176
## uc009dmb.2 8.53513 70.8126 0.490722 0.588398
## uc009eet.1 8.90222 23.2913 0.291414 1.000000
## short.mean.gp1 long.mean.gp1 short.mean.gp2 long.mean.gp2
## <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 33.53134 282.82402 1.05215 189.1411
## uc009dhz.2 520.91479 208.02460 172.15235 456.5446
## uc009dmb.2 8.85786 8.53513 49.53562 70.8126
## uc009eet.1 21.64617 8.90222 0.00000 23.2913
## PDUI.gp1 PDUI.gp2 dPDUI logFC P.Value adj.P.Val
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## uc009daz.2 0.894007 0.994468 -0.1004606 -0.153638 1.58698e-06 2.11597e-06
## uc009dhz.2 0.285380 0.726176 -0.4407961 -1.347436 1.70948e-60 6.83790e-60
## uc009dmb.2 0.490722 0.588398 -0.0976754 -0.261885 5.93031e-01 5.93031e-01
## uc009eet.1 0.291414 1.000000 -0.7085863 -1.778859 4.13501e-08 8.27003e-08
## PASS
## <logical>
## uc009daz.2 FALSE
## uc009dhz.2 TRUE
## uc009dmb.2 FALSE
## uc009eet.1 TRUE
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe steps described above can be done in one function call.
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 also 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 7 with logLik: -555.5656
## converged at iteration 1 with logLik: -188.938
## converged at iteration 23 with logLik: -152.6663
## converged at iteration 1 with logLik: -462.3804
## converged at iteration 10 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 17843.7079
## uc009dhz.2 uc009dhz.2 74244 Atg7 FALSE 7630.4981
## uc009die.2 uc009die.2 232334 Vgll4 FALSE 10704.6989
## uc009dmb.2 uc009dmb.2 211187 Lrtm2 FALSE 18.6351
## uc009eet.1 uc009eet.1 232406 BC035044 FALSE 227.5418
## uc009eol.1 uc009eol.1 11569 Aebp2 TRUE 204539.6984
## 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,... 29.66026
## uc009dhz.2 114859343 114860614 184,184,184,... 3.72545
## uc009die.2 114862164 114862092 76,76,76,... 121.88814
## uc009dmb.2 119317055 119315133 11,12,12,... 9.22610
## uc009eet.1 128850081 128848044 22,22,22,... 22.04495
## uc009eol.1 140651362 140651622 1408,1412,1384,... 1065.98013
## Baf3_long.form.usage Baf3_PDUI short.mean long.mean PDUI
## <numeric> <numeric> <list> <list> <list>
## uc009daz.2 283.38939 0.905254 29.6603 283.389 0.905254
## uc009dhz.2 209.97881 0.982567 3.72545 209.979 0.982567
## uc009die.2 174.12289 0.588231 121.888 174.123 0.588231
## uc009dmb.2 9.11799 0.497053 9.2261 9.11799 0.497053
## uc009eet.1 10.32742 0.319020 22.045 10.3274 0.31902
## uc009eol.1 221.45614 0.172013 1065.98 221.456 0.172013
## P.Value adj.P.Val dPDUI PASS
## <list> <list> <numeric> <logical>
## uc009daz.2 1 1 NaN FALSE
## uc009dhz.2 1 1 NaN FALSE
## uc009die.2 1 1 NaN FALSE
## uc009dmb.2 1 1 NaN FALSE
## uc009eet.1 1 1 NaN FALSE
## uc009eol.1 1 1 NaN FALSE
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthssessionInfo()R version 4.0.0 (2020-04-24) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.4 LTS
Matrix products: default BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so LAPACK: /home/biocbuild/bbs-3.11-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_1.26.0
[2] org.Hs.eg.db_3.10.0
[3] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[4] BSgenome.Mmusculus.UCSC.mm10_1.4.0
[5] BSgenome_1.56.0
[6] rtracklayer_1.48.0
[7] Biostrings_2.56.0
[8] XVector_0.28.0
[9] InPAS_1.20.0
[10] GenomicFeatures_1.40.0
[11] AnnotationDbi_1.50.0
[12] GenomicRanges_1.40.0
[13] GenomeInfoDb_1.24.0
[14] IRanges_2.22.0
[15] S4Vectors_0.26.0
[16] Biobase_2.48.0
[17] BiocGenerics_0.34.0
[18] BiocStyle_2.16.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 seqinr_3.6-1
[3] ellipsis_0.3.0 class_7.3-17
[5] depmixS4_1.4-2 biovizBase_1.36.0
[7] htmlTable_1.13.3 base64enc_0.1-3
[9] dichromat_2.0-0 rstudioapi_0.11
[11] bit64_0.9-7 splines_4.0.0
[13] knitr_1.28 ade4_1.7-15
[15] Formula_1.2-3 Rsamtools_2.4.0
[17] cluster_2.1.0 dbplyr_1.4.3
[19] png_0.1-7 BiocManager_1.30.10
[21] compiler_4.0.0 httr_1.4.1
[23] backports_1.1.6 assertthat_0.2.1
[25] Matrix_1.2-18 lazyeval_0.2.2
[27] limma_3.44.0 acepack_1.4.1
[29] htmltools_0.4.0 prettyunits_1.1.1
[31] tools_4.0.0 gtable_0.3.0
[33] glue_1.4.0 GenomeInfoDbData_1.2.3
[35] dplyr_0.8.5 rappdirs_0.3.1
[37] Rcpp_1.0.4.6 vctrs_0.2.4
[39] preprocessCore_1.50.0 nlme_3.1-147
[41] xfun_0.13 stringr_1.4.0
[43] lifecycle_0.2.0 ensembldb_2.12.0
[45] XML_3.99-0.3 zlibbioc_1.34.0
[47] MASS_7.3-51.6 scales_1.1.0
[49] VariantAnnotation_1.34.0 hms_0.5.3
[51] ProtGenerics_1.20.0 SummarizedExperiment_1.18.0
[53] AnnotationFilter_1.12.0 RColorBrewer_1.1-2
[55] yaml_2.2.1 curl_4.3
[57] memoise_1.1.0 gridExtra_2.3
[59] ggplot2_3.3.0 biomaRt_2.44.0
[61] rpart_4.1-15 latticeExtra_0.6-29
[63] stringi_1.4.6 RSQLite_2.2.0
[65] e1071_1.7-3 checkmate_2.0.0
[67] BiocParallel_1.22.0 truncnorm_1.0-8
[69] rlang_0.4.5 pkgconfig_2.0.3
[71] matrixStats_0.56.0 bitops_1.0-6
[73] Rsolnp_1.16 evaluate_0.14
[75] lattice_0.20-41 purrr_0.3.4
[77] GenomicAlignments_1.24.0 htmlwidgets_1.5.1
[79] bit_1.1-15.2 tidyselect_1.0.0
[81] magrittr_1.5 bookdown_0.18
[83] R6_2.4.1 Hmisc_4.4-0
[85] DelayedArray_0.14.0 DBI_1.1.0
[87] pillar_1.4.3 foreign_0.8-79
[89] BSgenome.Drerio.UCSC.danRer7_1.4.0 survival_3.1-12
[91] RCurl_1.98-1.2 nnet_7.3-14
[93] tibble_3.0.1 crayon_1.3.4
[95] BiocFileCache_1.12.0 rmarkdown_2.1
[97] jpeg_0.1-8.1 progress_1.2.2
[99] grid_4.0.0 data.table_1.12.8
[101] blob_1.2.1 digest_0.6.25
[103] openssl_1.4.1 munsell_0.5.0
[105] Gviz_1.32.0 askpass_1.1
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