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 genome

Bind 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.

## [[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 genome

This 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.