erma: epigenomics road map adventures
August 2015
Contents
1 Introduction
The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin.
This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.
2 Metadata on the ChromImpute archive
2.1 Sample metadata
I have retrieved a Google Docs spreadsheet with comprehensive information. The mapmeta() function provides access to a local DataFrame image of the file as retrieved in mid April 2015. We provide a dynamic view of a selection of columns. Use the search box to filter records shown, for example .
## NOTE: input data had non-ASCII characters replaced by ' '.2.2 Metadata on the inferred states
The chromatin states and standard colorings
used are enumerated in states_25:
The emission parameters of the 25 state model are depicted in the supplementary Figure 33 of Ernst and Kellis:
3 Managing access to imputed chromatin states for a set of cell types
I have retrieved a modest number of roadmap bed files with ChromImpute
mnemonic labeling of chromatin by states. These can be
managed with an ErmaSet instance,
a trivial extension of GenomicFiles class.
The cellTypes method yields a character vector. The colData
component has full metadata on the cell lines available.
## NOTE: input data had non-ASCII characters replaced by ' '.## ErmaSet object with 0 ranges and 31 files:
## files: E002_25_imputed12marks_mnemonics.bed.gz, E003_25_imputed12marks_mnemonics.bed.gz, ..., E088_25_imputed12marks_mnemonics.bed.gz, E096_25_imputed12marks_mnemonics.bed.gz
## detail: use files(), rowRanges(), colData(), ...
## cellTypes() for type names; data(short_celltype) for abbr.## [1] "ES-WA7 Cells"
## [2] "H1 Cells"
## [3] "iPS DF 6.9 Cells"
## [4] "Primary B cells from peripheral blood"
## [5] "Primary T cells from cord blood"4 Enumerating states in the vicinity of a gene, across cell types
We form a GRanges representing 50kb upstream of IL33.
## 'select()' returned 1:many mapping between keys and columns## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr9 [6165786, 6215785] +
## -------
## seqinfo: 1 sequence from hg19 genomeBind this to the ErmaSet instance.
## ErmaSet object with 1 ranges and 31 files:
## files: E002_25_imputed12marks_mnemonics.bed.gz, E003_25_imputed12marks_mnemonics.bed.gz, ..., E088_25_imputed12marks_mnemonics.bed.gz, E096_25_imputed12marks_mnemonics.bed.gz
## detail: use files(), rowRanges(), colData(), ...
## cellTypes() for type names; data(short_celltype) for abbr.Now query the files for cell-specific states in this interval.
library(BiocParallel)
register(MulticoreParam(workers=2)) # reduce will be done according to registered bpparam; lapply just extracts
suppressWarnings({
csstates = lapply(reduceByFile(ermaset, MAP=function(range, file) {
imp = import(file, which=range, genome=genome(range)[1])
seqlevels(imp) = seqlevels(range)
imp$rgb = erma:::rgbByState(imp$name)
imp
}), "[[", 1)
})
tys = cellTypes(ermaset) # need to label with cell types
csstates = lapply(1:length(csstates), function(x) {
csstates[[x]]$celltype = tys[x]
csstates[[x]]
})
csstates[1:2]## [[1]]
## GRanges object with 15 ranges and 3 metadata columns:
## seqnames ranges strand | name rgb
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr9 [6161801, 6166600] * | 25_Quies #FEFEFE
## [2] chr9 [6166601, 6166800] * | 17_EnhW2 #FEFE00
## [3] chr9 [6166801, 6171200] * | 25_Quies #FEFEFE
## [4] chr9 [6171201, 6171800] * | 17_EnhW2 #FEFE00
## [5] chr9 [6171801, 6172000] * | 16_EnhW1 #FEFE00
## ... ... ... ... . ... ...
## [11] chr9 [6183401, 6197400] * | 25_Quies #FEFEFE
## [12] chr9 [6197401, 6197600] * | 19_DNase #FEFE66
## [13] chr9 [6197601, 6208800] * | 25_Quies #FEFEFE
## [14] chr9 [6208801, 6211000] * | 21_Het #8990CF
## [15] chr9 [6211001, 6217800] * | 25_Quies #FEFEFE
## celltype
## <character>
## [1] ES-WA7 Cells
## [2] ES-WA7 Cells
## [3] ES-WA7 Cells
## [4] ES-WA7 Cells
## [5] ES-WA7 Cells
## ... ...
## [11] ES-WA7 Cells
## [12] ES-WA7 Cells
## [13] ES-WA7 Cells
## [14] ES-WA7 Cells
## [15] ES-WA7 Cells
## -------
## seqinfo: 1 sequence from hg19 genome
##
## [[2]]
## GRanges object with 14 ranges and 3 metadata columns:
## seqnames ranges strand | name rgb
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr9 [6161801, 6166600] * | 25_Quies #FEFEFE
## [2] chr9 [6166601, 6166800] * | 17_EnhW2 #FEFE00
## [3] chr9 [6166801, 6171200] * | 25_Quies #FEFEFE
## [4] chr9 [6171201, 6173000] * | 17_EnhW2 #FEFE00
## [5] chr9 [6173001, 6175400] * | 21_Het #8990CF
## ... ... ... ... . ... ...
## [10] chr9 [6183401, 6197400] * | 25_Quies #FEFEFE
## [11] chr9 [6197401, 6197600] * | 19_DNase #FEFE66
## [12] chr9 [6197601, 6209000] * | 25_Quies #FEFEFE
## [13] chr9 [6209001, 6211000] * | 21_Het #8990CF
## [14] chr9 [6211001, 6218200] * | 25_Quies #FEFEFE
## celltype
## <character>
## [1] H1 Cells
## [2] H1 Cells
## [3] H1 Cells
## [4] H1 Cells
## [5] H1 Cells
## ... ...
## [10] H1 Cells
## [11] H1 Cells
## [12] H1 Cells
## [13] H1 Cells
## [14] H1 Cells
## -------
## seqinfo: 1 sequence from hg19 genomeThis sort of code underlies
the csProfile utility to visualize variation in state assignments
in promoter regions for various genes.
## 'select()' returned 1:many mapping between keys and columns## Warning: executing %dopar% sequentially: no parallel backend registered## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
Set useShiny to TRUE to permit interactive selection of
region to visualize.