Use cases for coordinate mapping with ensembldb
3 April 2019
Package
ensembldb 2.6.8
This documents describes two use cases for the coordinate system mapping
functionality of ensembldb: mapping of regions within protein sequences to the
genome and mapping of genomic to protein sequence-relative coordinates. In
addition, it showcases the advanced filtering capabilities implemented in
ensembldb.
1 Query for helix-loop-helix transcription factors on chromosome 21
Down syndrome is a genetic disorder characterized by the presence of all or parts of a third copy of chromosome 21. It is associated, among other, with characteristic facial features and mild to moderate intellectual disability. The phenotypes are most likely the result from a gene dosage-dependent increased expression of the genes encoded on chromosome 21 (Lana-Elola et al. 2011). Compared to other gene classes, transcription factors are more likely to have an immediate impact, even due to a moderate over-expression (which might be the result from gene duplication). One of the largest dimerizing transcription factor families is characterized by a basic helix-loop-helix domain (Massari and Murre 2000), a protein structural motif facilitating DNA binding.
The example below aims at identifying transcription factors with a basic
helix-loop-helix domain (Pfam ID PF00010) that are encoded on chromosome 21. To
this end we first load an R-library providing human annotations from Ensembl
release 86 and pass the loaded EnsDb object along with a filter expression to
the genes method that retrieves the corresponding genes. Filter expressions
have to be written in the form ~ <field> <condition> <value> with <field>
representing the database column to be used for the filter. Several such filter
expressions can be concatenated with standard R logical expressions (such as &
or |). To get a list of all available filters and their corresponding fields,
the supportedFilters(edb) function could be used.
library(ensembldb)
library(EnsDb.Hsapiens.v86)
edb <- EnsDb.Hsapiens.v86
## Retrieve the genes
gns <- genes(edb, filter = ~ protein_domain_id == "PF00010" & seq_name == "21")The function returned a GRanges object with the genomic position of the genes
and additional gene-related annotations stored in metadata columns.
gns## GRanges object with 3 ranges and 7 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000205927 21 33025845-33029196 + | ENSG00000205927
## ENSG00000184221 21 33070144-33072420 + | ENSG00000184221
## ENSG00000159263 21 36699133-36749917 + | ENSG00000159263
## gene_name gene_biotype seq_coord_system symbol
## <character> <character> <character> <character>
## ENSG00000205927 OLIG2 protein_coding chromosome OLIG2
## ENSG00000184221 OLIG1 protein_coding chromosome OLIG1
## ENSG00000159263 SIM2 protein_coding chromosome SIM2
## entrezid protein_domain_id
## <list> <character>
## ENSG00000205927 10215 PF00010
## ENSG00000184221 116448 PF00010
## ENSG00000159263 6493 PF00010
## -------
## seqinfo: 1 sequence from GRCh38 genomeThree transcription factors with a helix-loop-helix domain are encoded on
chromosome 21: SIM2, which is a master regulator of neurogenesis and is
thought to contribute to some specific phenotypes of Down syndrome
(Gardiner and Costa 2006) and the two genes OLIG1 and OLIG2 for which genetic
triplication was shown to cause developmental brain defects
(Chakrabarti et al. 2010). To visualize the exonic regions encoding the
helix-loop-helix domain of these genes we next retrieve their transcript models
and the positions of all Pfam protein domains within the amino acid sequences of
encoded by these transcripts. We process SIM2 separately from OLIG1 and
OLIG2 because the latter are encoded in a narrow region on chromosome 21 and
can thus be visualized easily within the same plot. We extract the transcript
models for OLIG1 and OLIG2 that encode the protein domain using the
getGeneRegionTrackForGviz function which returns the data in a format that can
be directly passed to functions from the Gviz Bioconductor package
(Hahne and Ivanek 2016) for plotting. Since Gviz expects UCSC-style chromosome names
instead of the Ensembl chromosome names (e.g. chr21 instead of 21), we
change the format in which chromosome names are returned by ensembldb with the
seqlevelsStyle method. All subsequent queries to the EnsDb database will
return chromosome names in UCSC format.
## Change chromosome naming style to UCSC
seqlevelsStyle(edb) <- "UCSC"## Retrieve the transcript models for OLIG1 and OLIG2 that encode the
## the protein domain
txs <- getGeneRegionTrackForGviz(
edb, filter = ~ genename %in% c("OLIG1", "OLIG2") &
protein_domain_id == "PF00010")Next we fetch the coordinates of all Pfam protein domains encoded by these
transcripts with the proteins method, asking for columns "prot_dom_start",
"prot_dom_end" and "protein_domain_id" to be returned by the function. Note
that we restrict the results in addition to protein domains defined in Pfam.
pdoms <- proteins(edb, filter = ~ tx_id %in% txs$transcript &
protein_domain_source == "pfam",
columns = c("protein_domain_id", "prot_dom_start",
"prot_dom_end"))
pdoms## DataFrame with 3 rows and 6 columns
## protein_domain_id prot_dom_start prot_dom_end protein_id
## <character> <integer> <integer> <character>
## 1 PF00010 107 164 ENSP00000371785
## 2 PF00010 110 162 ENSP00000371794
## 3 PF00010 110 162 ENSP00000331040
## tx_id protein_domain_source
## <character> <character>
## 1 ENST00000382348 pfam
## 2 ENST00000382357 pfam
## 3 ENST00000333337 pfamWe next map these protein-relative positions to the genome. We define first an
IRanges object with the coordinates and submit this to the proteinToGenome
function for mapping. Besides coordinates, the function requires also the
respective protein identifiers which we supply as names.
pdoms_rng <- IRanges(start = pdoms$prot_dom_start, end = pdoms$prot_dom_end,
names = pdoms$protein_id)
pdoms_gnm <- proteinToGenome(pdoms_rng, edb)The result is a list of GRanges objects with the genomic coordinates at
which the protein domains are encoded, one for each of the input protein
domains. Additional information such as the protein ID, the encoding transcript
and the exons of the respective transcript in which the domain is encoded are
provided as metadata columns.
pdoms_gnm## $ENSP00000371785
## GRanges object with 1 range and 7 metadata columns:
## seqnames ranges strand | protein_id tx_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr21 33070565-33070738 + | ENSP00000371785 ENST00000382348
## exon_id exon_rank cds_ok protein_start protein_end
## <character> <integer> <logical> <integer> <integer>
## [1] ENSE00001491811 1 TRUE 107 164
## -------
## seqinfo: 1 sequence from GRCh38 genome
##
## $ENSP00000371794
## GRanges object with 1 range and 7 metadata columns:
## seqnames ranges strand | protein_id tx_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr21 33027190-33027348 + | ENSP00000371794 ENST00000382357
## exon_id exon_rank cds_ok protein_start protein_end
## <character> <integer> <logical> <integer> <integer>
## [1] ENSE00001491833 2 TRUE 110 162
## -------
## seqinfo: 1 sequence from GRCh38 genome
##
## $ENSP00000331040
## GRanges object with 1 range and 7 metadata columns:
## seqnames ranges strand | protein_id tx_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr21 33027190-33027348 + | ENSP00000331040 ENST00000333337
## exon_id exon_rank cds_ok protein_start protein_end
## <character> <integer> <logical> <integer> <integer>
## [1] ENSE00001379164 1 TRUE 110 162
## -------
## seqinfo: 1 sequence from GRCh38 genomeColumn cds_ok in the result object indicates whether the length of the CDS of
the encoding transcript matches the length of the protein sequence. For
transcripts with unknown 3’ and/or 5’ CDS ends these will differ. The mapping
result has to be re-organized before being plotted: Gviz expects a single
GRanges object, with specific metadata columns for the grouping of the
individual genomic regions. This is performed in the code block below.
## Convert the list to a GRanges with grouping information
pdoms_gnm_grng <- unlist(GRangesList(pdoms_gnm))
pdoms_gnm_grng$id <- rep(pdoms$protein_domain_id, lengths(pdoms_gnm))
pdoms_gnm_grng$grp <- rep(1:nrow(pdoms), lengths(pdoms_gnm))
pdoms_gnm_grng## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand | protein_id
## <Rle> <IRanges> <Rle> | <character>
## ENSP00000371785 chr21 33070565-33070738 + | ENSP00000371785
## ENSP00000371794 chr21 33027190-33027348 + | ENSP00000371794
## ENSP00000331040 chr21 33027190-33027348 + | ENSP00000331040
## tx_id exon_id exon_rank cds_ok
## <character> <character> <integer> <logical>
## ENSP00000371785 ENST00000382348 ENSE00001491811 1 TRUE
## ENSP00000371794 ENST00000382357 ENSE00001491833 2 TRUE
## ENSP00000331040 ENST00000333337 ENSE00001379164 1 TRUE
## protein_start protein_end id grp
## <integer> <integer> <character> <integer>
## ENSP00000371785 107 164 PF00010 1
## ENSP00000371794 110 162 PF00010 2
## ENSP00000331040 110 162 PF00010 3
## -------
## seqinfo: 1 sequence from GRCh38 genomeWe next define the individual tracks we want to visualize and plot them with the
plotTracks function from the Gviz package.
library(Gviz)
## Define the individual tracks:
## - Ideagram
ideo_track <- IdeogramTrack(genome = "hg38", chromosome = "chr21")
## - Genome axis
gaxis_track <- GenomeAxisTrack()
## - Transcripts
gene_track <- GeneRegionTrack(txs, showId = TRUE, just.group = "right",
name = "", geneSymbol = TRUE, size = 0.5)
## - Protein domains
pdom_track <- AnnotationTrack(pdoms_gnm_grng, group = pdoms_gnm_grng$grp,
id = pdoms_gnm_grng$id, groupAnnotation = "id",
just.group = "right", shape = "box",
name = "Protein domains", size = 0.5)
## Generate the plot
plotTracks(list(ideo_track, gaxis_track, gene_track, pdom_track))
Figure 1: Transcripts of genes OLIG1 and OLIG2 encoding a helix-loop-helix protein domain
Shown are all transcripts of the genes OLIG1 and OLIG2 that encode a protein with a helix-loop-helix protein domain (PF00010). Genomic positions encoding protein domains defined in Pfam are shown in light blue.
All transcripts are relatively short with the full coding region being in a single exon. Also, both transcripts encode a protein with a single protein domain, the helix-loop-helix domain PF00010.
Next we repeat the analysis for SIM2 by first fetching all of its transcript variants encoding the PF00010 Pfam protein domain from the database. Subsequently we retrieve all Pfam protein domains encoded in these transcripts.
## Fetch all SIM2 transcripts encoding PF00010
txs <- getGeneRegionTrackForGviz(edb, filter = ~ genename == "SIM2" &
protein_domain_id == "PF00010")
## Fetch all Pfam protein domains within these transcripts
pdoms <- proteins(edb, filter = ~ tx_id %in% txs$transcript &
protein_domain_source == "pfam",
columns = c("protein_domain_id", "prot_dom_start",
"prot_dom_end"))At last we have to map the protein domain coordinates to the genome and prepare the data for the plot. Since the code is essentially identical to the one for OLIG1 and OLIG2 it is not displayed.
Figure 2: Transcripts of the gene SIM2 encoding a helix-loop-helix domain
Shown are all transcripts of SIM2 encoding a protein with a helix-loop-helix protein domain (PF00010). Genomic positions encoding protein domains defined in Pfam are shown in light blue.
The SIM2 transcript encodes a protein with in total 4 protein domains. The helix-loop-helix domain PF00010 is encoded in its first exon.
2 Mapping of genomic coordinates to protein-relative positions
One of the known mutations for human red hair color is located at position
16:89920138 (dbSNP ID rs1805009) on the human genome (version GRCh38). Below we
map this genomic coordinate to the respective coordinate within the protein
sequence encoded at that location using the genomeToProtein function. Note
that we use "chr16" as the name of the chromosome, since we changed the
chromosome naming style to UCSC in the previous example.
gnm_pos <- GRanges("chr16", IRanges(89920138, width = 1))
prt_pos <- genomeToProtein(gnm_pos, edb)
prt_pos## IRangesList of length 1
## [[1]]
## IRanges object with 3 ranges and 9 metadata columns:
## start end width | tx_id cds_ok
## <integer> <integer> <integer> | <character> <logical>
## ENSP00000451605 294 294 1 | ENST00000555147 TRUE
## ENSP00000451760 294 294 1 | ENST00000555427 TRUE
## ENSP00000451560 294 294 1 | ENST00000556922 TRUE
## tx_id exon_id exon_rank seq_start
## <character> <character> <integer> <integer>
## ENSP00000451605 ENST00000555147 ENSE00002458332 1 89920138
## ENSP00000451760 ENST00000555427 ENSE00002477640 3 89920138
## ENSP00000451560 ENST00000556922 ENSE00002507842 1 89920138
## seq_end seq_name seq_strand
## <integer> <character> <character>
## ENSP00000451605 89920138 chr16 *
## ENSP00000451760 89920138 chr16 *
## ENSP00000451560 89920138 chr16 *The genomic position could thus be mapped to the amino acid 294 in each of the 3
proteins listed above. Using the select function we retrieve the official
symbol of the gene for these 3 proteins.
select(edb, keys = ~ protein_id == names(prt_pos[[1]]), columns = "SYMBOL")## SYMBOL PROTEINID
## 1 RP11-566K11.2 ENSP00000451560
## 2 MC1R ENSP00000451605
## 3 MC1R ENSP00000451760Two proteins are from the MC1R gene and one from RP11-566K11.2 (ENSG00000198211) a gene which exons overlap exons from MC1R as well as exons of the more downstream located gene TUBB3. To visualize this we first fetch transcripts overlapping the genomic position of interest and subsequently all additional transcripts within the region defined by the most downstream and upstream exons of the transcripts.
## Get transcripts overlapping the genomic position.
txs <- getGeneRegionTrackForGviz(edb, filter = GRangesFilter(gnm_pos))
## Get all transcripts within the region from the start of the most 5'
## and end of the most 3' exon.
all_txs <- getGeneRegionTrackForGviz(
edb, filter = GRangesFilter(range(txs), type = "within"))
## Plot the data
## - Ideogram
ideo_track <- IdeogramTrack(genome = "hg38", chromosome = "chr16")
## - Genome axis
gaxis_track <- GenomeAxisTrack()
## - Transcripts
gene_track <- GeneRegionTrack(all_txs, showId = TRUE, just.group = "right",
name = "", geneSymbol = TRUE, size = 0.5)
## - highlight the region.
hl_track <- HighlightTrack(list(gaxis_track, gene_track), range = gnm_pos)
## Generate the plot
plotTracks(list(ideo_track, hl_track))
Figure 3: Transcripts overlapping, or close to, the genomic position of interest
Shown are all transcripts The genomic position of the variant is highlighted in red.
In the plot above we see 4 transcripts for which one exon overlaps the genomic
position of the variant: two of the gene MC1R, one of RP11-566K11.2 and one
of RP11-566K11.4, a non-coding gene encoded on the reverse strand. Using the
proteins method we next extract the sequences of the proteins encoded by the 3
transcripts on the forward strand and determine the amino acid at position 294
in these. To retrieve the results in a format most suitable for the
representation of amino acid sequences we specify return.type = "AAStringSet"
in the proteins call.
## Get the amino acid sequences for the 3 transcripts
prt_seq <- proteins(edb, return.type = "AAStringSet",
filter = ~ protein_id == names(prt_pos[[1]]))
## Extract the amino acid at position 294
subseq(prt_seq, start = 294, end = 294)## A AAStringSet instance of length 3
## width seq names
## [1] 1 D ENSP00000451560
## [2] 1 D ENSP00000451605
## [3] 1 D ENSP00000451760The amino acid at position 294 is for all an aspartic acid (“D”) which is in agreement with the reference amino acid of mutation Asp294His (Valverde et al. 1995) described by the dbSNP ID of this example.
3 Session information
sessionInfo()## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-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] grid stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] Gviz_1.26.5 EnsDb.Hsapiens.v86_2.99.0
## [3] ensembldb_2.6.8 AnnotationFilter_1.6.0
## [5] GenomicFeatures_1.34.7 AnnotationDbi_1.44.0
## [7] Biobase_2.42.0 GenomicRanges_1.34.0
## [9] GenomeInfoDb_1.18.2 IRanges_2.16.0
## [11] S4Vectors_0.20.1 BiocGenerics_0.28.0
## [13] BiocStyle_2.10.0
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## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.14.0 bitops_1.0-6
## [3] matrixStats_0.54.0 bit64_0.9-7
## [5] RColorBrewer_1.1-2 progress_1.2.0
## [7] httr_1.4.0 tools_3.5.3
## [9] backports_1.1.3 R6_2.4.0
## [11] rpart_4.1-13 Hmisc_4.2-0
## [13] DBI_1.0.0 lazyeval_0.2.2
## [15] colorspace_1.4-1 nnet_7.3-12
## [17] tidyselect_0.2.5 gridExtra_2.3
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## [29] stringr_1.4.0 digest_0.6.18
## [31] Rsamtools_1.34.1 foreign_0.8-71
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## [35] dichromat_2.0-0 base64enc_0.1-3
## [37] pkgconfig_2.0.2 htmltools_0.3.6
## [39] highr_0.8 BSgenome_1.50.0
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## [47] dplyr_0.8.0.1 VariantAnnotation_1.28.13
## [49] RCurl_1.95-4.12 magrittr_1.5
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## [59] zlibbioc_1.28.0 plyr_1.8.4
## [61] blob_1.1.1 crayon_1.3.4
## [63] lattice_0.20-38 Biostrings_2.50.2
## [65] splines_3.5.3 hms_0.4.2
## [67] knitr_1.22 pillar_1.3.1
## [69] biomaRt_2.38.0 XML_3.98-1.19
## [71] glue_1.3.1 evaluate_0.13
## [73] biovizBase_1.30.1 latticeExtra_0.6-28
## [75] data.table_1.12.0 BiocManager_1.30.4
## [77] gtable_0.3.0 purrr_0.3.2
## [79] assertthat_0.2.1 ggplot2_3.1.0
## [81] xfun_0.6 survival_2.44-1.1
## [83] tibble_2.1.1 GenomicAlignments_1.18.1
## [85] memoise_1.1.0 cluster_2.0.7-1References
Chakrabarti, Lina, Tyler K Best, Nathan P Cramer, Rosalind S E Carney, John T R Isaac, Zygmunt Galdzicki, and Tarik F Haydar. 2010. “Olig1 and Olig2 triplication causes developmental brain defects in Down syndrome.” Nature Neuroscience 13 (8):927–34.
Gardiner, Katheleen, and Alberto C S Costa. 2006. “The proteins of human chromosome 21.” American Journal of Medical Genetics. Part C, Seminars in Medical Genetics 142C (3):196–205.
Hahne, Florian, and Robert Ivanek. 2016. “Visualizing Genomic Data Using Gviz and Bioconductor.” Methods in Molecular Biology (Clifton, N.J.) 1418 (Chapter 16):335–51.
Lana-Elola, Eva, Sheona D Watson-Scales, Elizabeth M C Fisher, and Victor L J Tybulewicz. 2011. “Down syndrome: searching for the genetic culprits.” Disease Models & Mechanisms 4 (5):586–95.
Massari, M E, and C Murre. 2000. “Helix-loop-helix proteins: regulators of transcription in eucaryotic organisms.” Molecular and Cellular Biology 20 (2):429–40.
Valverde, P, E Healy, I Jackson, J L Rees, and A J Thody. 1995. “Variants of the melanocyte-stimulating hormone receptor gene are associated with red hair and fair skin in humans.” Nature Genetics 11 (3):328–30.