2021-10-13_FANCM-manuscript-Crossovers-genomic-features
Ruqian Lyu
10/13/2021
Last updated: 2021-10-13
Checks: 6 1
Knit directory: yeln_2019_spermtyping/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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Crossover distributions and genomic features
SNP density distribution indicate how many informative SNPs we have to map crossovers in either the scCNV or BC1F1 bulk sequencing samples. We are interested to find out whether our ability to find crossovers are impacted by the SNP densities or not.
SNP density distribution
SNP density distribution found by looking at the positions of SNPs from the reference VCF file references/FVB_NJ.mgp.v5.snps.dbSNP142.homo.alt.chr.vcf.gz
snp_density <- read.table(file = "references/FVB_NJ.mgp.v5.snps.dbSNP142.homo.alt.chr.txt",
col.names = c("chr","Pos"))
snp_density <- snp_density[!snp_density$chr %in% c("chrX","chrY"),]TSS density
Using biomRt for finding transcript starting sizes for mouse (ref genome mm10)
ensembl95 <- useEnsembl(biomart = 'genes',
dataset = 'mmusculus_gene_ensembl',
version = 95)
tss_annot <- getBM( attributes = c("transcription_start_site", "chromosome_name",
"transcript_start", "transcript_end",
"strand", "ensembl_gene_id",
"ensembl_transcript_id", "external_gene_name"),
mart = ensembl95)
tss_annot <- tss_annot[tss_annot$chromosome_name %in% paste0(1:19),]GC Content TSS and SNP density scCNV
mm10.gc5Base.wig.gz downloaded from https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/
mm10_meanGC <- read.table(file = "data/mm10.1m.meanGC.bed")
mm10_meanGC$V4 <- as.numeric(mm10_meanGC$V4)
#plotTracks(list(mm10_meanGC_track,crossover_count_track),chromosome = "chr1")Construct GRanges for genomic features.
# tss_track <- DataTrack(range = GRanges(seqnames = tss_annot$chromosome_name,
# ranges = IRanges(start = tss_annot$transcription_start_site,
# width = 1)),
# genome = "mm10",
# data = data.frame(count = rep(1, length(tss_annot$chromosome_name))),
# type="hist",
# window = 80,
# aggregation = sum,
# name = "TSS\ndensity",
# background.panel = "#f3f4d3",
# background.title = "#d3e3b6",
# cex.title = 1.4,
# cex.axis = 1.0)
tss_gr <- GRanges(seqnames = tss_annot$chromosome_name,
ranges = IRanges(start = tss_annot$transcription_start_site,
width = 1),
tss_count = 1)
snp_density_gr <- GRanges(seqnames = snp_density$chr,
ranges = IRanges(start = snp_density$Pos,
width = 1),
snp_count = 1)
mm10_meanGC_gr <- GRanges(seqnames = mm10_meanGC$V1,
ranges = IRanges(start = mm10_meanGC$V2,
end = mm10_meanGC$V3),
gc_perc = as.numeric( mm10_meanGC$V4))
# mm10_meanGC_track <- DataTrack(range = GRanges(seqnames = mm10_meanGC$V1,
# ranges = IRanges(start = mm10_meanGC$V2,
# end = mm10_meanGC$V3)),
# genome = "mm10",
# data = mm10_meanGC$V4,
# type=c("p","l"),
# window = 80,
# aggregation = mean,
# name = "GC\nPercent",
# background.panel = "#FFFEDB",
# background.title = "lightblue",
# cex.title = 1.4,
# cex.axis = 1.0)
# snp_density_track_log <- DataTrack(range = GRanges(seqnames = snp_density$chr,
# ranges = IRanges(start = snp_density$Pos,
# width = 1)),
# genome = "mm10",
# data = data.frame(count = rep(1, length(snp_density$chr))),
# type=c("p","l"),
# window = 98,
# aggregation = function(x) { log10(sum(x)+1)},
# name = "SNP\ndensity\nlog10",
# background.panel = "#FFFEDB",
# background.title = "lightblue",
# cex.title = 1.4,
# cex.axis = 1.0)
#plotTracks(snp_density_track_log,chromosome = "chr1", main = "SNP denstiy chr1")Single-sperm dataset
scCNV <-
readRDS(file = "~/Projects/rejy_2020_single-sperm-co-calling/output/outputR/analysisRDS/allSamples.setting4.rds")
x <- c("mutant","mutant","wildtype","mutant",
"wildtype","wildtype")
xx <- c("Fancm-/-","Fancm-/-","Fancm+/+","Fancm-/-",
"Fancm+/+","Fancm+/+")
crossover_counts <- scCNV
crossover_counts$sampleType <- plyr::mapvalues(crossover_counts$sampleGroup,from = c("WC_522",
"WC_526",
"WC_CNV_42",
"WC_CNV_43",
"WC_CNV_44",
"WC_CNV_53"),
to =x)
gtrack <- GenomeAxisTrack(cex.axis = 1.0)
sc_crossover_gr <- GRanges(seqnames = seqnames(crossover_counts),
ranges = ranges(crossover_counts))
per_cell_cos <- as.matrix(assay(crossover_counts))
per_sample_type_cos <- sapply(unique(crossover_counts$sampleType), function(sampleType){
rowMeans(per_cell_cos[,which(colData(crossover_counts)$sampleType == sampleType)])
})
mcols(sc_crossover_gr) <- per_sample_type_cos
# crossover_count_track <- DataTrack(range = rowRanges(crossover_counts),
# genome = "mm10",
# data = data.frame(assay(crossover_counts)),
# name = "mean crossover counts\nacross windows",
# type = "heatmap",
# groups = crossover_counts$sampleType,
# col = c("cornflowerblue","tan1"),
# aggregateGroups = TRUE,
# aggregation = mean,
# background.title = "pink",
# window = 80,
# cex.title = 1.4,
# cex.axis = 1.0)Distribute genomic feature counts into equal sized bins
cal_bin_dist <- function(new_gr,bin_size,
ref_genome="mm10"){
## bin_size supplied then.
## fetch the chromoInfo from GenomeInfoDb.
## This is only for getting the basepair lengths of the genome
chrom_info <- GenomeInfoDb::getChromInfoFromUCSC(ref_genome)
## only for chr1-M
chrom_info <- chrom_info[grep("_",chrom_info$chrom,invert = TRUE),]
## Check what seqnames is in new_gr and make it consistent
if(!grepl("chr",as.character(seqnames(new_gr)[1]))){
chrom_info$chrom <- gsub("chr","",chrom_info$chrom)
}
chrom_info <- chrom_info[chrom_info$chrom %in%
GenomeInfoDb::seqlevels(new_gr),]
## create Granges object for chromosomes
seq_length <- chrom_info$size
names(seq_length) <- chrom_info$chrom
dna_mm10_gr <- GenomicRanges::GRanges(
seqnames = Rle(names(seq_length)),
ranges = IRanges(1, end = seq_length, names = names(seq_length)),
seqlengths = seq_length)
GenomeInfoDb::genome(dna_mm10_gr) <- ref_genome
#dna_mm10_gr
## per bp distances
GenomicRanges::mcols(new_gr) <- apply(GenomicRanges::mcols(new_gr),2,
function(x) {
x/GenomicRanges::width(new_gr)})
tilewidth <- bin_size
tiles <- GenomicRanges::tileGenome(seqinfo(dna_mm10_gr),
tilewidth = tilewidth)
binned_dna_mm10_gr <- unlist(tiles)
new_gr <- GenomicRanges::sort(GenomeInfoDb::sortSeqlevels(new_gr))
if(!is.null(new_gr$gc_perc)){
new_gr <- subset(new_gr,!is.nan(new_gr$gc_perc))
}
bin_dist <- lapply(colnames(mcols(new_gr)), function(group_col){
dist_rle <- GenomicRanges::coverage(new_gr,
weight = mcols(new_gr)[,group_col],
)
dist_bined <- binnedAverage(binned_dna_mm10_gr,dist_rle,
"dist_bin_ave")
return(dist_bined$dist_bin_ave*width(dist_bined))
})
mcols(binned_dna_mm10_gr) <- do.call(cbind,bin_dist)
colnames(mcols(binned_dna_mm10_gr)) <- colnames(mcols(new_gr))
binned_dna_mm10_gr
}Generate DataTracks from bin_gr
bin_size <- 1e6
bin_gr <- cal_bin_dist(sc_crossover_gr,bin_size = bin_size)
meanGC_bin_gr <- cal_bin_dist(mm10_meanGC_gr,bin_size = bin_size)
tss_bin_gr <- cal_bin_dist(tss_gr,bin_size = bin_size)
snp_bin_gr <- cal_bin_dist(snp_density_gr,bin_size = bin_size)
geneGFTracks <- function(){
co_track <- DataTrack(range = bin_gr,
genome = "mm10",
data = data.frame(mcols(bin_gr)),
name = "scCNV",
type = "heatmap",
groups = c("mutant","wildtype"),
col = c("cornflowerblue","tan1"),
aggregateGroups = TRUE,
aggregation = mean,
background.title = "pink",
# window = -1,
cex.title = 1.4,
cex.axis = 1.0)
gc_track <- DataTrack(range = meanGC_bin_gr,
genome = "mm10",
data = data.frame(mcols(meanGC_bin_gr)),
name = "GC percent",
type = c("p","l"),
aggregation = mean,
background.panel = "#FFFEDB",
background.title = "lightblue",
# window = -1,
cex.title = 1.4,
cex.axis = 1.0)
snp_density_track_log <- DataTrack(range =snp_bin_gr,
genome = "mm10",
data = data.frame(log10(mcols(snp_bin_gr)[,1]+1)),
type=c("p","l"),
# window = 98,
# aggregation = function(x) { log10(sum(x)+1)},
name = "SNP\ndensity\nlog10",
background.panel = "#FFFEDB",
background.title = "lightblue",
cex.title = 1.4,
cex.axis = 1.0)
tss_track <- DataTrack(range =tss_bin_gr,
genome = "mm10",
data = data.frame(mcols(tss_bin_gr)),
type="hist",
# window = 80,
aggregation = sum,
name = "TSS\ndensity",
background.panel = "#f3f4d3",
background.title = "#d3e3b6",
cex.title = 1.4,
cex.axis = 1.0)
list(gtrack,tss_track,gc_track,snp_density_track_log,
co_track)
}scCNV_alltracks <- geneGFTracks()#c(1,8,9,11)
chroms <- paste0("chr",1:19)
ncols <- 2
nrows <- 3
for(i in seq_along(chroms)){
if(i%%(ncols * nrows)==1) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrows,ncols)))
}
k <- i%%(ncols * nrows)
k <- ifelse(k==0,ncols * nrows,k)
pushViewport(viewport(layout.pos.col = ((k - 1) %% ncols) + 1,
layout.pos.row = (((k) - 1) %/% ncols) + 1))
plotTracks(scCNV_alltracks,
chromosome = chroms[i],
add = TRUE,
main = chroms[i], sizes=c(1,2,2,2,2))
popViewport(1)
}
scCNV Scatter plots of Crossovers versus SNP density
crossover_gr <- GRanges(seqnames = seqnames(crossover_counts),
ranges = ranges(crossover_counts))
per_cell_cos <- as.matrix(assay(crossover_counts))
per_sample_type_cos <- sapply(unique(crossover_counts$sampleType), function(sampleType){
rowMeans(per_cell_cos[,which(colData(crossover_counts)$sampleType == sampleType)])
})
mcols(crossover_gr) <- per_sample_type_cos
plts_list <- list()
i <- 1
for(bin_size in c(1e3,1e4,1e5,1e6,1.5e6,1e7)){
crossover_bin_gr <- cal_bin_dist(crossover_gr,bin_size = bin_size)
snp_gr <- GRanges(seqnames = snp_density$chr,
ranges = IRanges(start = snp_density$Pos,
width = 1),
count = 1)
snp_bin_gr <- cal_bin_dist(snp_gr,bin_size = bin_size)
snp_den_crossover_corr <- crossover_bin_gr
mcols(snp_den_crossover_corr) <- cbind(mcols(snp_bin_gr),mcols(crossover_bin_gr))
snp_den_crossover_corr_df <- data.frame(snp_den_crossover_corr)
snp_den_crossover_corr_df$diff <- (snp_den_crossover_corr_df$mutant - snp_den_crossover_corr_df$wildtype)
plot_df <- snp_den_crossover_corr_df %>% dplyr::mutate(bin_type = case_when(
count ==0 ~ "Zero SNPs",
count < 10 ~ "1-10 SNPs",
count <=50 ~ "11-50 SNPs",
count <=100 ~ "51-100 SNPs",
TRUE ~ "High SNPs"
))
try( p <- plot_df %>% dplyr::filter(count>0) %>%
ggplot()+geom_point(mapping = aes(x = count, y = diff,color = bin_type))+
theme_bw(base_size = 18)+
geom_hline(mapping = aes(yintercept=0))+
scale_x_log10(),TRUE)
try(plts_list[[i]] <- p+facet_wrap(.~seqnames),TRUE)
i = i+1
}plts_list[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
Crossovers per scCNV sample
crossover_count_track_perSample <- DataTrack(range = rowRanges(crossover_counts),
genome = "mm10",
data = data.frame(assay(crossover_counts)),
name ="expected crossover counts across chromsome windows",
type = "heatmap",
aggregateGroups=TRUE,
groups = crossover_counts$sampleGroup)
chroms <- paste0("chr",1:19)
ncols <- 2
nrows <- 2
for(i in seq_along(chroms)){
if(i%%(ncols * nrows)==1) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrows,ncols)))
}
k <- i%%(ncols * nrows)
k <- ifelse(k==0,ncols * nrows,k)
pushViewport(viewport(layout.pos.col = ((k - 1) %% ncols) + 1,
layout.pos.row = (((k) - 1) %/% ncols) + 1))
plotTracks(list(gtrack,crossover_count_track_perSample),
chromosome = chroms[i],add = TRUE,
main = chroms[i])
popViewport(1)
}
BC1F1 samples
bc1f1_samples <- readRDS(file = "output/outputR/analysisRDS/all_rse_count_07-20.rds")#table(bc1f1_samples$sampleGroup)
bc1f1_samples_male <- bc1f1_samples[,bc1f1_samples$sampleGroup %in% c( "Male_HET","Male_KO","Male_WT")]
bc1f1_samples_female <- bc1f1_samples[,bc1f1_samples$sampleGroup %in% c( "Female_HET","Female_KO","Female_WT")]Male
male_crossover_gr <- GRanges(seqnames = seqnames(bc1f1_samples_male),
ranges = ranges(bc1f1_samples_male))
per_cell_cos <- as.matrix(assay(bc1f1_samples_male))
per_sample_type_cos <- sapply(unique(bc1f1_samples_male$sampleGroup), function(sampleGroup){
rowMeans(per_cell_cos[,which(colData(bc1f1_samples_male)$sampleGroup == sampleGroup)])
})
mcols(male_crossover_gr) <- per_sample_type_cos
bin_size <- 1e6
male_bin_gr <- cal_bin_dist(male_crossover_gr,bin_size = bin_size)
male_co_track <- DataTrack(range = male_bin_gr,
genome = "mm10",
data = data.frame(mcols(male_bin_gr)),
name = "BC1F1 male",
type = "heatmap",
groups = colnames(mcols(male_bin_gr)),
col = c("Male_HET"="grey","Male_KO"="cornflowerblue",
"Male_WT"="tan1"),
aggregateGroups = TRUE,
aggregation = mean,
background.title = "pink",
# window = -1,
cex.title = 1.4,
cex.axis = 1.0)
male_alltracks <- scCNV_alltracks
male_alltracks[[5]] <- male_co_track#c(1,8,9,11)
chroms <- paste0("chr",1:19)
ncols <- 2
nrows <- 3
for(i in seq_along(chroms)){
if(i%%(ncols * nrows)==1) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrows,ncols)))
}
k <- i%%(ncols * nrows)
k <- ifelse(k==0,ncols * nrows,k)
pushViewport(viewport(layout.pos.col = ((k - 1) %% ncols) + 1,
layout.pos.row = (((k) - 1) %/% ncols) + 1))
plotTracks(male_alltracks,
chromosome = chroms[i],
add = TRUE,
main = chroms[i], sizes=c(1,2,2,2,2))
popViewport(1)
}
Female
female_crossover_gr <- GRanges(seqnames = seqnames(bc1f1_samples_female),
ranges = ranges(bc1f1_samples_female))
per_cell_cos <- as.matrix(assay(bc1f1_samples_female))
per_sample_type_cos <- sapply(unique(bc1f1_samples_female$sampleGroup), function(sampleGroup){
rowMeans(per_cell_cos[,which(colData(bc1f1_samples_female)$sampleGroup == sampleGroup)])
})
mcols(female_crossover_gr) <- per_sample_type_cos
bin_size <- 1e6
female_bin_gr <- cal_bin_dist(female_crossover_gr,bin_size = bin_size)
female_co_track <- DataTrack(range = female_bin_gr,
genome = "mm10",
data = data.frame(mcols(female_bin_gr)),
name = "BC1F1 female",
type = "heatmap",
groups = colnames(mcols(female_bin_gr)),
col = c("Female_HET"="grey","Female_KO"="cornflowerblue",
"Female_WT"="tan1"),
aggregateGroups = TRUE,
aggregation = mean,
background.title = "pink",
# window = -1,
cex.title = 1.4,
cex.axis = 1.0)
female_alltracks <- scCNV_alltracks
female_alltracks[[5]] <- female_co_track#c(1,8,9,11)
chroms <- paste0("chr",1:19)
ncols <- 2
nrows <- 3
for(i in seq_along(chroms)){
if(i%%(ncols * nrows)==1) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrows,ncols)))
}
k <- i%%(ncols * nrows)
k <- ifelse(k==0,ncols * nrows,k)
pushViewport(viewport(layout.pos.col = ((k - 1) %% ncols) + 1,
layout.pos.row = (((k) - 1) %/% ncols) + 1))
plotTracks(female_alltracks,
chromosome = chroms[i],
add = TRUE,
main = chroms[i], sizes=c(1,2,2,2,2))
popViewport(1)
}
Session info
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.4 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] biomaRt_2.48.3 doParallel_1.0.16
[3] iterators_1.0.13 foreach_1.5.1
[5] Gviz_1.36.2 gridExtra_2.3
[7] SummarizedExperiment_1.22.0 Biobase_2.52.0
[9] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[11] IRanges_2.26.0 S4Vectors_0.30.1
[13] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[15] matrixStats_0.61.0 dplyr_1.0.7
[17] comapr_0.99.27 ggrepel_0.9.1
[19] ggplot2_3.3.5 readxl_1.3.1
loaded via a namespace (and not attached):
[1] backports_1.2.1 circlize_0.4.13 Hmisc_4.5-0
[4] workflowr_1.6.2 BiocFileCache_2.0.0 plyr_1.8.6
[7] lazyeval_0.2.2 splines_4.1.0 BiocParallel_1.26.2
[10] digest_0.6.28 ensembldb_2.16.4 htmltools_0.5.2
[13] fansi_0.5.0 magrittr_2.0.1 checkmate_2.0.0
[16] memoise_2.0.0 BSgenome_1.60.0 cluster_2.1.2
[19] Biostrings_2.60.2 prettyunits_1.1.1 jpeg_0.1-9
[22] colorspace_2.0-2 blob_1.2.2 rappdirs_0.3.3
[25] xfun_0.26 crayon_1.4.1 RCurl_1.98-1.5
[28] jsonlite_1.7.2 survival_3.2-11 VariantAnnotation_1.38.0
[31] glue_1.4.2 gtable_0.3.0 zlibbioc_1.38.0
[34] XVector_0.32.0 DelayedArray_0.18.0 shape_1.4.6
[37] scales_1.1.1 DBI_1.1.1 Rcpp_1.0.7
[40] viridisLite_0.4.0 progress_1.2.2 htmlTable_2.2.1
[43] foreign_0.8-81 bit_4.0.4 Formula_1.2-4
[46] htmlwidgets_1.5.4 httr_1.4.2 RColorBrewer_1.1-2
[49] ellipsis_0.3.2 farver_2.1.0 pkgconfig_2.0.3
[52] XML_3.99-0.8 nnet_7.3-16 dbplyr_2.1.1
[55] utf8_1.2.2 labeling_0.4.2 tidyselect_1.1.1
[58] rlang_0.4.11 reshape2_1.4.4 later_1.3.0
[61] AnnotationDbi_1.54.1 munsell_0.5.0 cellranger_1.1.0
[64] tools_4.1.0 cachem_1.0.6 generics_0.1.0
[67] RSQLite_2.2.8 evaluate_0.14 stringr_1.4.0
[70] fastmap_1.1.0 yaml_2.2.1 knitr_1.36
[73] bit64_4.0.5 fs_1.5.0 purrr_0.3.4
[76] KEGGREST_1.32.0 AnnotationFilter_1.16.0 xml2_1.3.2
[79] compiler_4.1.0 rstudioapi_0.13 plotly_4.9.4.1
[82] filelock_1.0.2 curl_4.3.2 png_0.1-7
[85] tibble_3.1.4 stringi_1.7.4 highr_0.9
[88] GenomicFeatures_1.44.2 lattice_0.20-44 ProtGenerics_1.24.0
[91] Matrix_1.3-3 vctrs_0.3.8 pillar_1.6.3
[94] lifecycle_1.0.1 jquerylib_0.1.4 GlobalOptions_0.1.2
[97] data.table_1.14.2 bitops_1.0-7 httpuv_1.6.3
[100] rtracklayer_1.52.1 R6_2.5.1 BiocIO_1.2.0
[103] latticeExtra_0.6-29 promises_1.2.0.1 codetools_0.2-18
[106] dichromat_2.0-0 assertthat_0.2.1 rprojroot_2.0.2
[109] rjson_0.2.20 withr_2.4.2 GenomicAlignments_1.28.0
[112] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6 hms_1.1.1
[115] rpart_4.1-15 tidyr_1.1.4 rmarkdown_2.11
[118] git2r_0.28.0 biovizBase_1.40.0 base64enc_0.1-3
[121] restfulr_0.0.13
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.0 (2021-05-18)
os Rocky Linux 8.4 (Green Obsidian)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2021-10-13
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi 1.54.1 2021-06-08 [1] Bioconductor
AnnotationFilter 1.16.0 2021-05-19 [1] Bioconductor
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.1.0)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.1.0)
Biobase * 2.52.0 2021-05-19 [1] Bioconductor
BiocFileCache 2.0.0 2021-05-19 [1] Bioconductor
BiocGenerics * 0.38.0 2021-05-19 [1] Bioconductor
BiocIO 1.2.0 2021-05-19 [1] Bioconductor
BiocParallel 1.26.2 2021-08-22 [1] Bioconductor
biomaRt * 2.48.3 2021-08-15 [1] Bioconductor
Biostrings 2.60.2 2021-08-05 [1] Bioconductor
biovizBase 1.40.0 2021-05-19 [1] Bioconductor
bit 4.0.4 2020-08-04 [1] CRAN (R 4.1.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.1.0)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.1.0)
blob 1.2.2 2021-07-23 [1] CRAN (R 4.1.0)
BSgenome 1.60.0 2021-05-19 [1] Bioconductor
cachem 1.0.6 2021-08-19 [1] CRAN (R 4.1.0)
callr 3.7.0 2021-04-20 [1] CRAN (R 4.1.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.1.0)
checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.1.0)
circlize 0.4.13 2021-06-09 [1] CRAN (R 4.1.0)
cli 3.0.1 2021-07-17 [1] CRAN (R 4.1.0)
cluster 2.1.2 2021-04-17 [2] CRAN (R 4.1.0)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.0)
colorspace 2.0-2 2021-06-24 [1] CRAN (R 4.1.0)
comapr * 0.99.27 2021-09-30 [1] Github (ruqianl/comapr@870dfef)
crayon 1.4.1 2021-02-08 [1] CRAN (R 4.1.0)
curl 4.3.2 2021-06-23 [1] CRAN (R 4.1.0)
data.table 1.14.2 2021-09-27 [1] CRAN (R 4.1.0)
DBI 1.1.1 2021-01-15 [1] CRAN (R 4.1.0)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.1.0)
DelayedArray 0.18.0 2021-05-19 [1] Bioconductor
desc 1.4.0 2021-09-28 [1] CRAN (R 4.1.0)
devtools 2.4.2 2021-06-07 [1] CRAN (R 4.1.0)
dichromat 2.0-0 2013-01-24 [1] CRAN (R 4.1.0)
digest 0.6.28 2021-09-23 [1] CRAN (R 4.1.0)
doParallel * 1.0.16 2020-10-16 [1] CRAN (R 4.1.0)
dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.1.0)
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ensembldb 2.16.4 2021-08-05 [1] Bioconductor
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fansi 0.5.0 2021-05-25 [1] CRAN (R 4.1.0)
farver 2.1.0 2021-02-28 [1] CRAN (R 4.1.0)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.0)
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foreach * 1.5.1 2020-10-15 [1] CRAN (R 4.1.0)
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fs 1.5.0 2020-07-31 [1] CRAN (R 4.1.0)
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GenomeInfoDbData 1.2.6 2021-09-30 [1] Bioconductor
GenomicAlignments 1.28.0 2021-05-19 [1] Bioconductor
GenomicFeatures 1.44.2 2021-08-26 [1] Bioconductor
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ggrepel * 0.9.1 2021-01-15 [1] CRAN (R 4.1.0)
git2r 0.28.0 2021-01-10 [1] CRAN (R 4.1.0)
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glue 1.4.2 2020-08-27 [1] CRAN (R 4.1.0)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.1.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0)
Gviz * 1.36.2 2021-07-04 [1] Bioconductor
highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
Hmisc 4.5-0 2021-02-28 [1] CRAN (R 4.1.0)
hms 1.1.1 2021-09-26 [1] CRAN (R 4.1.0)
htmlTable 2.2.1 2021-05-18 [1] CRAN (R 4.1.0)
htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.1.0)
htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.1.0)
httpuv 1.6.3 2021-09-09 [1] CRAN (R 4.1.0)
httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.0)
IRanges * 2.26.0 2021-05-19 [1] Bioconductor
iterators * 1.0.13 2020-10-15 [1] CRAN (R 4.1.0)
jpeg 0.1-9 2021-07-24 [1] CRAN (R 4.1.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.1.0)
jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.1.0)
KEGGREST 1.32.0 2021-05-19 [1] Bioconductor
knitr 1.36 2021-09-29 [1] CRAN (R 4.1.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0)
later 1.3.0 2021-08-18 [1] CRAN (R 4.1.0)
lattice 0.20-44 2021-05-02 [2] CRAN (R 4.1.0)
latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.1.0)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.1.0)
lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.1.0)
Matrix 1.3-3 2021-05-04 [2] CRAN (R 4.1.0)
MatrixGenerics * 1.4.3 2021-08-26 [1] Bioconductor
matrixStats * 0.61.0 2021-09-17 [1] CRAN (R 4.1.0)
memoise 2.0.0 2021-01-26 [1] CRAN (R 4.1.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0)
nnet 7.3-16 2021-05-03 [2] CRAN (R 4.1.0)
pillar 1.6.3 2021-09-26 [1] CRAN (R 4.1.0)
pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.1.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0)
pkgload 1.2.2 2021-09-11 [1] CRAN (R 4.1.0)
plotly 4.9.4.1 2021-06-18 [1] CRAN (R 4.1.0)
plyr 1.8.6 2020-03-03 [1] CRAN (R 4.1.0)
png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.1.0)
processx 3.5.2 2021-04-30 [1] CRAN (R 4.1.0)
progress 1.2.2 2019-05-16 [1] CRAN (R 4.1.0)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.1.0)
ProtGenerics 1.24.0 2021-05-19 [1] Bioconductor
ps 1.6.0 2021-02-28 [1] CRAN (R 4.1.0)
purrr 0.3.4 2020-04-17 [1] CRAN (R 4.1.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.1.0)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.1.0)
Rcpp 1.0.7 2021-07-07 [1] CRAN (R 4.1.0)
RCurl 1.98-1.5 2021-09-17 [1] CRAN (R 4.1.0)
readxl * 1.3.1 2019-03-13 [1] CRAN (R 4.1.0)
remotes 2.4.1 2021-09-29 [1] CRAN (R 4.1.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.1.0)
restfulr 0.0.13 2017-08-06 [1] CRAN (R 4.1.0)
rjson 0.2.20 2018-06-08 [1] CRAN (R 4.1.0)
rlang 0.4.11 2021-04-30 [1] CRAN (R 4.1.0)
rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.1.0)
rpart 4.1-15 2019-04-12 [2] CRAN (R 4.1.0)
rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.1.0)
Rsamtools 2.8.0 2021-05-19 [1] Bioconductor
RSQLite 2.2.8 2021-08-21 [1] CRAN (R 4.1.0)
rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
rtracklayer 1.52.1 2021-08-15 [1] Bioconductor
S4Vectors * 0.30.1 2021-09-26 [1] Bioconductor
scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.1.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
stringi 1.7.4 2021-08-25 [1] CRAN (R 4.1.0)
stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
SummarizedExperiment * 1.22.0 2021-05-19 [1] Bioconductor
survival 3.2-11 2021-04-26 [2] CRAN (R 4.1.0)
testthat 3.0.4 2021-07-01 [1] CRAN (R 4.1.0)
tibble 3.1.4 2021-08-25 [1] CRAN (R 4.1.0)
tidyr 1.1.4 2021-09-27 [1] CRAN (R 4.1.0)
tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0)
usethis 2.0.1 2021-02-10 [1] CRAN (R 4.1.0)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
VariantAnnotation 1.38.0 2021-05-19 [1] Bioconductor
vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0)
viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.1.0)
withr 2.4.2 2021-04-18 [1] CRAN (R 4.1.0)
workflowr 1.6.2 2020-04-30 [1] CRAN (R 4.1.0)
xfun 0.26 2021-09-14 [1] CRAN (R 4.1.0)
XML 3.99-0.8 2021-09-17 [1] CRAN (R 4.1.0)
xml2 1.3.2 2020-04-23 [1] CRAN (R 4.1.0)
XVector 0.32.0 2021-05-19 [1] Bioconductor
yaml 2.2.1 2020-02-01 [1] CRAN (R 4.1.0)
zlibbioc 1.38.0 2021-05-19 [1] Bioconductor
[1] /mnt/beegfs/mccarthy/scratch/general/rlyu/Software/R/Rlib/4.1.0/yeln
[2] /opt/R/4.1.0/lib/R/library