---
title: "Using drawProteins"
author:
- name: "Dr Paul Brennan"
affiliation: 
- "Centre for Medical Education, School of Medicine, Cardiff University, 
    Cardiff, Wales, United Kingdom"
email: BrennanP@cardiff.ac.uk
package: drawProteins
date: "`r Sys.Date()`"
output: BiocStyle::html_document
vignette: >
    %\VignetteIndexEntry{Using drawProteins}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
---

```{r load_packages, eval = TRUE, echo=FALSE}
library(BiocStyle)
library(drawProteins)
library(httr)
library(ggplot2)
library(knitr)
opts_chunk$set(comment=NA,
                fig.align = "center",
                out.width = "100%",
                dpi = 100)
```

# 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. 

# 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:
```{r download_rel_json, eval=TRUE, echo=TRUE}
# accession numbers of rel A
    drawProteins::get_features("Q04206") ->
    rel_json
```

# 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. 

```{r generate_dataframe}
drawProteins::feature_to_dataframe(rel_json) -> rel_data

# show in console
head(rel_data[1:4])

```


# 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.

```{r using_draw_canvas, fig.height=3, fig.wide = TRUE}
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`.
```{r using draw_chains, fig.height=3, fig.wide = TRUE}
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`.
```{r using draw_domains, fig.height=3, fig.wide = TRUE}
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:
```{r white_background, fig.height=3, fig.wide = TRUE}
# 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
```


# Checking the other features

```{r show_draw_regions, fig.height=3, fig.wide = TRUE}
draw_regions(p, rel_data) # adds activation domain

```

```{r show_draw_repeat, fig.height=3, fig.wide = TRUE}
draw_repeat(p, rel_data) # doesn't add anything in this case

```

```{r show_draw_motif, fig.height=3, fig.wide = TRUE}
draw_motif(p, rel_data) # adds 9aa Transactivation domain & NLS
```


```{r show_draw_phospho, fig.height=3, fig.wide = TRUE}
# add phosphorylation sites from Uniprot
draw_phospho(p, rel_data, size = 8)
```

# 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.
```{r relA_workflow, fig.height=3.5, fig.wide = TRUE}
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
```

### Adding titles to the plots 

Using ggplot2 then allows the addition of titles:
```{r add_titles, fig.height=4, fig.wide = TRUE}
# 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
```


# Drawing schematic for multiple proteins

With internet access, the script below shows the workflow for five proteins of
the NFkappaB transcription factor family. 
```{r five_NFkappaB, fig.height=10, fig.wide = TRUE}
# accession numbers of five NF-kappaB proteins
prot_data <- drawProteins::get_features("Q04206 Q01201 Q04864 P19838 Q00653")
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
```


# Customising the draw functions
Currently, it's possible to customise the chain colour and outline. It's 
possible to remove the labels. 

```{r customising, fig.height=6, fig.wide = TRUE}
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.

```{r custom_phospho, fig.height=8, fig.wide = TRUE}
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. 
```{r change_labels, fig.height=8, fig.wide = TRUE}
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()
```

# Session info
Here is the output of `sessionInfo()` on the system on which this document was
compiled:
```{r session_Info, echo=FALSE}
sessionInfo()
```
