Using drawProteins
2019-10-29
Package
drawProteins 1.6.0
Contents
1 Overview of drawProteins
This package has been created to allow the creation of protein schematics based on the data obtained from the Uniprot Protein Database.
The basic workflow is: 1. to provide one or more Uniprot IDs 2. get a list of feature from the Uniprot API 3. draw the basic chains of these proteins 4. add features as desired
drawProteins uses the package httr to interact with the Uniprot API and extract a JSON object into R. The JSON object is used to create a data.table.
The graphing package ggplot2 is then used to create the protein schematic.
2 Getting the data from Uniprot
Currently, drawProteins interacts with the [Uniprot database]http://www.uniprot.org/. At least one working Uniprot accession numbers must be provided. More than one can be provided but they must be separated by a single space. The spaces are replaced to create an url that can be used to query the Uniprot API
The get_features() function uses the Uniprot API to return the features of a
protein - the chain, domain information and other annotated features such as
“repeats” and “motifs”. Post-translational modifications, such as
phosphorylations, are also provided.
The httr::content() function is then used to extract the content. From the
get_features() function, this will provide lists of lists. The length of the
parent lists corresponds to the number of accession numbers provided.
Interestingly, the order sometimes appears different to that provided. Each of
lists inside the parent list are a list of six - one for each protein - that
contains names of the proteins and the features.
As an example, we will retrieve the details of a protein called Rel A or NF-kappaB, p65, a well studied transcription factor.
With internet access, this can be retreived from Uniprot with this code:
# accession numbers of rel A
drawProteins::get_features("Q04206") ->
rel_json[1] "Download has worked"3 Turning Uniprot data into a dataframe
The next step in the workflow is to convert the data from the Uniprot API into a dataframe that can be used with ggplot2.
The feature_to_dataframe() function will convert the list of lists of six
provided by the get_features() function to a dataframe which can then be
used to plot the schematics.
The feature_to_dataframe() function will also add an “order” value to allow
plotting. The order goes from the bottom in the manner of a graph.
drawProteins::feature_to_dataframe(rel_json) -> rel_data
# show in console
head(rel_data[1:4]) type description begin end
featuresTemp CHAIN Transcription factor p65 1 551
featuresTemp.1 DOMAIN RHD 19 306
featuresTemp.2 REGION Transcriptional activation domain 3 342 389
featuresTemp.3 REGION Transcriptional activation domain 1 415 476
featuresTemp.4 REGION Transcriptional activation domain 2 520 551
featuresTemp.5 MOTIF Nuclear localization signal 301 3044 Draw the protein chains and domains
The data can be plotted with ggplot2 using the geom_rect() and geom_label.
The first step is to make canvas with draw_canvas which is based on the
longest protein that is being drawin. This can be done using a pipe in the
following way.
draw_canvas(rel_data) -> p
p
Then we can plot the protein chain. We use the draw_chain() function to which
we have to provide the ggplot object p and the data which is
called rel_data.
p <- draw_chains(p, rel_data)
p
Now, we add the domains which are drawn to scale in terms of their lengths. We
use the draw_domains() function to which we have to provide the
ggplot object p and the data which is called rel_data.
The default is to label the chains. The labels can be removed using the
argument label_chains = FALSE.
p <- draw_domains(p, rel_data)
p
To show this visualisation better, a white background helps as well as removing the y-axis and the grid. Also changing the size of the text using the base_size argument. This can be done with this code:
# white background and remove y-axis
p <- p + theme_bw(base_size = 20) + # white background
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank()) +
theme(axis.ticks = element_blank(),
axis.text.y = element_blank()) +
theme(panel.border = element_blank())
p
5 Checking the other features
draw_regions(p, rel_data) # adds activation domain
draw_repeat(p, rel_data) # doesn't add anything in this case
draw_motif(p, rel_data) # adds 9aa Transactivation domain & NLS
# add phosphorylation sites from Uniprot
draw_phospho(p, rel_data, size = 8)
6 Putting it all together
In this way it’s possible to chose the geoms that give the information desired in the way you like. Some customisation is possible as described below.
For Rel A, my recommendation would be the following workflow.
draw_canvas(rel_data) -> p
p <- draw_chains(p, rel_data)
p <- draw_domains(p, rel_data)
p <- draw_regions(p, rel_data)
p <- draw_motif(p, rel_data)
p <- draw_phospho(p, rel_data, size = 8)
p <- p + theme_bw(base_size = 20) + # white backgnd & change text size
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank()) +
theme(axis.ticks = element_blank(),
axis.text.y = element_blank()) +
theme(panel.border = element_blank())
p
6.0.1 Adding titles to the plots
Using ggplot2 then allows the addition of titles:
# add titles
rel_subtitle <- paste0("circles = phosphorylation sites\n",
"RHD = Rel Homology Domain\nsource:Uniprot")
p <- p + labs(title = "Rel A/p65",
subtitle = rel_subtitle)
p
7 Drawing schematic for multiple proteins
With internet access, the script below shows the workflow for five proteins of the NFkappaB transcription factor family.
# accession numbers of five NF-kappaB proteins
prot_data <- drawProteins::get_features("Q04206 Q01201 Q04864 P19838 Q00653")[1] "Download has worked"prot_data <- drawProteins::feature_to_dataframe(prot_data)
p <- draw_canvas(prot_data)
p <- draw_chains(p, prot_data)
p <- draw_domains(p, prot_data)
p <- draw_repeat(p, prot_data)
p <- draw_motif(p, prot_data)
p <- draw_phospho(p, prot_data, size = 8)
# background and y-axis
p <- p + theme_bw(base_size = 20) + # white backgnd & change text size
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank()) +
theme(axis.ticks = element_blank(),
axis.text.y = element_blank()) +
theme(panel.border = element_blank())
# add titles
rel_subtitle <- paste0("circles = phosphorylation sites\n",
"RHD = Rel Homology Domain\nsource:Uniprot")
p <- p + labs(title = "Schematic of human NF-kappaB proteins",
subtitle = rel_subtitle)
# move legend to top
p <- p + theme(legend.position="top") + labs(fill="")
p
8 Customising the draw functions
Currently, it’s possible to customise the chain colour and outline. It’s possible to remove the labels.
data("five_rel_data")
p <- draw_canvas(five_rel_data)
p <- draw_chains(p, five_rel_data,
label_chains = FALSE,
fill = "hotpink",
outline = "midnightblue")
p
It’s also possible to change the size and colour of the phosphorylation symbols.
p <- draw_canvas(five_rel_data)
p <- draw_chains(p, five_rel_data,
fill = "lightsteelblue1",
outline = "grey",
label_size = 5)
p <- draw_phospho(p, five_rel_data, size = 10, fill = "red")
p + theme_bw()
It’s also possible to change the labels to a custom list. But remember that the plots are drawn from the bottom up.
p <- draw_canvas(five_rel_data)
p <- draw_chains(p, five_rel_data,
fill = "lightsteelblue1",
outline = "grey",
labels = c("p50/p105",
"p50/p105",
"p52/p100",
"p52/p100",
"Rel B",
"c-Rel",
"p65/Rel A"),
label_size = 5)
p <- draw_phospho(p, five_rel_data, size = 8, fill = "red")
p + theme_bw()
9 Session info
Here is the output of sessionInfo() on the system on which this document was
compiled:
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.10-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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.25 ggplot2_3.2.1 httr_1.4.1
[4] drawProteins_1.6.0 BiocStyle_2.14.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 pillar_1.4.2 compiler_3.6.1
[4] BiocManager_1.30.9 tools_3.6.1 digest_0.6.22
[7] jsonlite_1.6 evaluate_0.14 tibble_2.1.3
[10] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.1
[13] curl_4.2 yaml_2.2.0 xfun_0.10
[16] withr_2.1.2 stringr_1.4.0 dplyr_0.8.3
[19] grid_3.6.1 tidyselect_0.2.5 glue_1.3.1
[22] R6_2.4.0 rmarkdown_1.16 bookdown_0.14
[25] purrr_0.3.3 magrittr_1.5 scales_1.0.0
[28] htmltools_0.4.0 assertthat_0.2.1 colorspace_1.4-1
[31] labeling_0.3 stringi_1.4.3 lazyeval_0.2.2
[34] munsell_0.5.0 crayon_1.3.4