---
title: "Use cases for coordinate mapping with ensembldb"
author: "Johannes Rainer, Laurent Gatto and Christian X. Weichenberger"
graphics: yes
package: ensembldb
output:
BiocStyle::html_document:
toc_float: true
vignette: >
%\VignetteIndexEntry{Use cases for coordinate mapping with ensembldb}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\VignetteDepends{ensembldb,EnsDb.Hsapiens.v86,BiocStyle,Gviz,Biostrings}
bibliography: references.bib
---
```{r biocstyle, echo = FALSE, results = "asis", message = FALSE }
library(BiocStyle)
BiocStyle::markdown()
```
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`.
# 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 [@LanaElola:2011fl]. 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:2000um], 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.
```{r load-libs, message = FALSE}
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.
```{r gene-GRanges}
gns
```
Three 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:2006uj] and the two genes _OLIG1_ and _OLIG2_ for which genetic
triplication was shown to cause developmental brain defects
[@Chakrabarti:2010dt]. 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:2016ha] 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.
```{r olig-tx-models, message = FALSE}
## Change chromosome naming style to UCSC
seqlevelsStyle(edb) <- "UCSC"
```
```{r edb-subset, message = FALSE, echo = FALSE}
## Subset the EnsDb to speed up vignette processing
edb <- filter(edb, filter = ~ seq_name %in% c("chr21", "chr16"))
```
```{r retrieve-olig-tx-models, message = FALSE}
## 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.
```{r olig-prot-doms, message = FALSE}
pdoms <- proteins(edb, filter = ~ tx_id %in% txs$transcript &
protein_domain_source == "pfam",
columns = c("protein_domain_id", "prot_dom_start",
"prot_dom_end"))
pdoms
```
We next map these protein-relative positions to the genome. We define first an
`IRanges` object with the coordinates and submit this to the `proteinToGenome`
function for mapping. Besides coordinates, the function requires also the
respective protein identifiers which we supply as names.
```{r olig-proteinToGenome, message = FALSE}
pdoms_rng <- IRanges(start = pdoms$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.
```{r olig-proteinToGenome-result, message = FALSE}
pdoms_gnm
```
Column `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.
```{r olig-proteinToGenome-reorganize, message = FALSE}
## 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$grp <- rep(1:nrow(pdoms), lengths(pdoms_gnm))
pdoms_gnm_grng
```
We next define the individual tracks we want to visualize and plot them with the
`plotTracks` function from the `Gviz` package.
```{r olig-plot, message = FALSE, warning = FALSE, fig.align = "center", fig.width = 8, fig.height = 2, fig.cap = "Transcripts of genes OLIG1 and OLIG2 encoding a helix-loop-helix protein domain. Shown are all transcripts of the genes OLIG1 and OLIG2 that encode a protein with a helix-loop-helix protein domain (PF00010). Genomic positions encoding protein domains defined in Pfam are shown in light blue.", fig.pos = "h!"}
library(Gviz)
## Define the individual tracks:
## - Ideogram
## ideo_track <- IdeogramTrack(genome = "hg38", chromosome = "chr21")
## - Genome axis
gaxis_track <- GenomeAxisTrack()
## - Transcripts
gene_track <- GeneRegionTrack(txs, showId = TRUE, just.group = "right",
name = "", geneSymbol = TRUE, size = 0.5)
## - Protein domains
pdom_track <- AnnotationTrack(pdoms_gnm_grng, group = pdoms_gnm_grng$grp,
id = pdoms_gnm_grng$id, groupAnnotation = "id",
just.group = "right", shape = "box",
name = "Protein domains", size = 0.5)
## Generate the plot
plotTracks(list(gaxis_track, gene_track, pdom_track))
```
All transcripts are relatively short with the full coding region being in a
single exon. Also, both transcripts encode a protein with a single protein
domain, the helix-loop-helix domain PF00010.
Next we repeat the analysis for _SIM2_ by first fetching all of its transcript
variants encoding the PF00010 Pfam protein domain from the
database. Subsequently we retrieve all Pfam protein domains encoded in these
transcripts.
```{r sim2-fetch, message = FALSE}
## Fetch all SIM2 transcripts encoding PF00010
txs <- getGeneRegionTrackForGviz(edb, filter = ~ genename == "SIM2" &
protein_domain_id == "PF00010")
## Fetch all Pfam protein domains within these transcripts
pdoms <- proteins(edb, filter = ~ tx_id %in% txs$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.
```{r sim2-plot, message = FALSE, warning = FALSE, fig.align = "center", fig.width = 8, fig.height = 2, fig.cap = "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.", echo = FALSE, fig.pos = "h!"}
pdoms_rng <- IRanges(start = pdoms$prot_dom_start, end = pdoms$prot_dom_end,
names = pdoms$protein_id)
pdoms_gnm <- proteinToGenome(pdoms_rng, edb)
## Convert the list to a GRanges with grouping information
pdoms_gnm_grng <- unlist(GRangesList(pdoms_gnm))
pdoms_gnm_grng$id <- rep(pdoms$protein_domain_id, lengths(pdoms_gnm))
pdoms_gnm_grng$grp <- rep(1:nrow(pdoms), lengths(pdoms_gnm))
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(gaxis_track, gene_track, pdom_track))
```
The _SIM2_ transcript encodes a protein with in total 4 protein domains. The
helix-loop-helix domain PF00010 is encoded in its first exon.
# 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.
```{r ex2-map, message = FALSE, warning = FALSE}
gnm_pos <- GRanges("chr16", IRanges(89920138, width = 1))
prt_pos <- genomeToProtein(gnm_pos, edb)
prt_pos
```
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.
```{r ex2-select, message = FALSE}
select(edb, keys = ~ protein_id == names(prt_pos[[1]]), columns = "SYMBOL")
```
Two 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.
```{r ex2-plot, message = FALSE, warning = FALSE, fig.cap = "Transcripts overlapping, or close to, the genomic position of interest. Shown are all transcripts The genomic position of the variant is highlighted in red.", fig.width = 8, fig.height = 4, fig.pos = "h!"}
## 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(hl_track))
```
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.
```{r ex2-res, message = FALSE}
## 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
library(Biostrings)
subseq(prt_seq, start = 294, end = 294)
```
The 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:1995if]
described by the dbSNP ID of this example.
# Session information
```{r sessionInfo}
sessionInfo()
```
# References