drawProteins: a Bioconductor/R package for reproducible and programmatic generation of protein schematics

Paul Brennan

doi:10.12688/f1000research.14541.1

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drawProteins: a Bioconductor/R package for reproducible and programmatic generation of protein schematics

[version 1; peer review: 2 approved]

Paul Brennan

PUBLISHED 18 Jul 2018

Author details Author details

Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, Wales, UK

Paul Brennan
Roles: Conceptualization, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

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This article is included in the Bioconductor gateway.

Abstract

Protein schematics are valuable for research, teaching and knowledge communication. However, the tools used to automate the process are challenging. The purpose of the drawProteins package is to enable the generation of schematics of proteins in an automated fashion that can integrate with the Bioconductor/R suite of tools for bioinformatics and statistical analysis. Using UniProt accession numbers, the package uses the UniProt API to get the features of the protein from the UniProt database. The features are assembled into a data frame and visualized using adaptations of the ggplot2 package. Visualizations can be customised in many ways including adding additional protein features information from other data frames, altering colors and protein names and adding extra layers using other ggplot2 functions. This can be completed within a script that makes the workflow reproducible and sharable.

Keywords

protein, schematic, BIOCONDUCTOR, R package, visualization.

Corresponding author: Paul Brennan

Competing interests: No competing interests were disclosed.

Grant information: PB has been supported by funding from Bloodwise, UK.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2018 Brennan P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Brennan P. drawProteins: a Bioconductor/R package for reproducible and programmatic generation of protein schematics [version 1; peer review: 2 approved]. F1000Research 2018, 7:1105 (https://doi.org/10.12688/f1000research.14541.1) First published: 18 Jul 2018, 7:1105 (https://doi.org/10.12688/f1000research.14541.1) Latest published: 18 Jul 2018, 7:1105 (https://doi.org/10.12688/f1000research.14541.1)

Introduction

Protein schematics are abundant in research papers, reviews, text books and on the internet¹. Thus, they represent one of the most common molecular visualizations shown and seen by researchers and students. Constructing protein schematics is often time consuming and is not performed in a reproducible and easily shared manner. The schematics frequently reflect domain expertise, but often also reflect the opinions of an individual researcher in a manner that is not obvious.

There are solutions in other languages: a Java and JavaScript tool^1,2 that can be used for protein visualization. For visualization on the internet, there is also the BioJS solution, which can be used for proteins³. Both of these tools are useful but not easily integrated into the Bioconductor workflow. The GenVisR package contains the option to produce highly customisable publication-quality graphics for genomic data⁴. The focus on genomic data reduces the usefulness of drawing protein schematics, particularly those illustrating multiple proteins and protein families.

For these reasons, a protein visualization package was produced using R to allow compatibility with the Bio- conductor suite of bioinformatics packages. It uses the UniProt Proteins API^5,6 as a resource of protein features and the ggplot2 package⁷ as a basis for drawing the schematics. Multiple proteins can be drawn from similar or different families. The only limitation is the availability of UniProt entries.

Schematic customisation is possible. Protein chains, domains, regions, motifs or phosphorylation sites can be drawn separately or together. Colors can be altered and protein names (labels) can be changed. All of this can be done in a scripted manner that facilitates code sharing, visualization reproducibility and good practice in scientific computing⁸.

Methods

System requirements

The Bioconductor pacakge, drawProteins, is designed to work with Bioconductor 3.7 and R version 3.5.

Implementation

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 of drawProteins is:

1. To provide one or more Uniprot IDs
2. Get a list of feature from the Uniprot API
3. Draw the chains of the 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 frame. Adaptations of the graphing package ggplot2 are then used to create the protein schematic.

Operation

Getting the data from Uniprot. Currently, drawProteins obtains the protein feature information from the UniProt Protein API^5,6. At least one working Uniprot accession number must be provided. More than one can be provided but they must be in the same vector, separated by a space. The space is replaced to create a url that can be used to query the Uniprot API⁹.

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 by the UniProt API.

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 is different to that of the UniProt accession numbers provided. The 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, the script below will retrieve, from UniProt, the details of a the human version of a protein called RelA or NF-kappaB, p65, a well-studied transcription factor¹⁰.

With internet access, this can be retrieved from Uniprot with this code:

library(drawProteins)
library(ggplot2)
# UniProt accession number for human rel A is Q04206
rel_json <- drawProteins::get_features("Q04206")

## [1] "Download has worked"

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.

rel_data <- drawProteins::feature_to_dataframe(rel_json)

The rel_data object is a data frame with 9 variables and observations that include protein features. The variables are show below. A data frame of this type could be created independently of UniProt.

str(rel_data)

## ’data.frame’:    69 obs. of 9 variables:
##  $ type       : chr  "CHAIN" "DOMAIN" "REGION" "MOTIF" ...
##  $ description: chr  "Transcription factor p65" "RHD" "Activation domain" "Nuclear localization signal" 
##  $ begin      : num  1 19 415 301 536 1 38 38 122 123 ...
##  $ end        : num  551 306 459 304 544 1 38 38 122 123 ...
##  $ length     : num  550 287 44 3 8 0 0 0 0 0 ...
##  $ accession  : chr  "Q04206" "Q04206" "Q04206" "Q04206" ...
##  $ entryName  : chr  "TF65_HUMAN" "TF65_HUMAN" "TF65_HUMAN" "TF65_HUMAN" ...
##  $ taxid      : int  9606 9606 9606 9606 9606 9606 9606 9606 9606 9606 ...
##  $ order      : int  1 1 1 1 1 1 1 1 1 1 ...

Draw the canvas, protein chains and domains. The first step is to create the plot area with the draw_canvas() function. The x-axis of the canvas is based on the length of the protein (or the longest protein in the case of drawing multiple proteins). The y-axis is based on the number of proteins being drawn. The draw_canvas() function requires a data frame.

Usually, the next step is to draw the protein chains using the draw_chains() function. This requires a ggplot2 object and a data frame in that order. The data frame does not have to be the same as that used for draw_canvas() but must contain the variables type, description, begin, end, entryName, order.

Protein domains can be added with the draw_domains() function, which also requires a ggplot2 object and a data frame in that order. Again, the data frame does not have to be the same as that obtained from UniProt but must contain the variables type, description, begin, end, and order. Thus custom domains can be added with the draw_domains() function. Note that the chain and the domain are drawn to scale in terms of their number of amino acids (Figure 1).

p <- draw_canvas(rel_data)
p <- draw_chains(p, rel_data, label_size = 2.5)
p <- draw_domains(p, rel_data)
p

Figure 1. Protein domain schematic of RelA/p65.

The default output gives a grey background and labels the domain. RHD = Rel Homology Domain.

To show this visualization better, a white background helps, as does removing the y-axis and the grid (Figure 2). Changing the size of the text using the base_size argument also aids visualization. This can be done with this code:

# white background and remove y-axis
p <- p + theme_bw(base_size = 10) + # 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

Figure 2. Protein domain schematic of RelA/p65.

The background can be customized using theme functions from ggplot2. RHD = Rel Homology Domain.

Use cases

Putting the RelA workflow together and adding titles

The UniProt API provides information on protein regions, protein motifs and protein phosphorylation sites. By using the functions shown in the script below, it is possible to show the features of the protein desired to create. Altering colors and adding customisation is possible.

For the human protein RelA, also known as the p65 subunit of NFkappaB, a transcription factor with diverse functions including a role in leukaemia, inflammation and cancer, here is a good workflow that generates a nice schematic of the protein showing domains and phosphorylation sites (Figure 3).

draw_canvas(rel_data) -> p
p <- draw_chains(p, rel_data, label_size = 2)
p <- draw_domains(p, rel_data)
p <- draw_regions(p, rel_data) # add regions
p <- draw_motif(p, rel_data)   # add motifs
p <- draw_phospho(p, rel_data, size = 3) # add phosphorylation sites

p <- p + theme_bw(base_size = 10) + # 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 = "Human Rel A/p65",
                subtitle = rel_subtitle)
p

Figure 3. More detailed protein domain schematic of RelA/p65.

By drawing the domains, regions and motifs a more detailed protein schematic is generated. RHD = Rel Homology Domain; TAD = Transactivation Domain. Yellow circles denote phosphorylation sites.

Drawing schematic for multiple proteins

With internet access, the script below shows the workflow and generates a visualization of the five human proteins of the NF-kappaB transcription factor family (Figure 4).

# 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, label_size = 2)
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 = 4)

# background and y-axis
p <- p + theme_bw(base_size = 10) + # 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

Figure 4. Protein domain schematic of human NF-kappaB proteins.

The five members of the NF-kappaB transcription factors family can be illustrated by drawing the domains, regions and motifs as detailed on the UniProt database. The lengths of the chains, domains and motifs are proportional to the number of amino acids. RHD = Rel Homology Domain; TAD = Transactivation Domain. Yellow circles denote phosphorylation sites.

The proteins, domains and phosphorylation sites (yellow circles) are drawn and positioned according to amino acid number.

Working with BioMart

It is possible to use bioMart^11,12 to pull out the UniProt accession numbers for a Gene Ontology (GO) term. For example, the GO term for “MAP kinase activity”. This has a GO number of GO:0004707. This example script borrows heavily on the biomaRt users guide written by Steffen Durinck, Wolfgang Huber and Mike Smith¹³. The script below generates a visualization containing 14 protein schematics (Figure 5).

# install bioMart if you haven’t used it before
# remove the hash tags...
# source("http://www.bioconductor.org/biocLite.R ")
# biocLite()
# biocLite("biomaRt")
library(biomaRt)
# chosing a database = MART and a dataset - gets more focussed each step...
ensembl = useMart("ensembl",
                    dataset="hsapiens_gene_ensembl")

# Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have
# a “MAP kinase activity” GO term associated with it.
# this is the GO:0004707
# this gives 14 proteins....
# create output in a dataframe and add uniprotswissprot
# which is the UniProt ID
output <- getBM(attributes = c(’uniprotswissprot’,
                                   ’hgnc_symbol’),
                  filters = ’go’,
                  values = ’GO:0004707’,
                  mart = ensembl)

# returns a dataframe... pull out uniprotIDs for drawing...
uniprotIDs <- output$uniprotswissprot
# get rid of blank entries - turn into NA
uniprotIDs[uniprotIDs==""] <- NA
# remove NA
uniprotIDs <- na.omit(uniprotIDs)
# make the IDs characters
uniprotIDs <- as.character(uniprotIDs)
# just the unique ones
uniprotIDs <- unique(uniprotIDs)
# combine into one element
uniprotIDs <- paste(uniprotIDs, collapse = " ")
# this can now be used in drawProteins

# now get features from Uniprot
library(magrittr)

uniprotIDs %>%
  drawProteins::get_features() %>%
  drawProteins::feature_to_dataframe() ->
  prot_data

## [1] "Download has worked"

# data frame with 722 observations

library(ggplot2)
# basic drawing
p <- draw_canvas(prot_data)
p <- draw_chains(p, prot_data, label_size = 2)
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 = 4)

# background and y-axis
p <- p + theme_bw(base_size = 10) + # white background and 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
p <- p + labs(title = "Schematic of human MAP kinases",
  subtitle = "circles = phosphorylation sites\nsource:Uniprot")
  
# move legend to top
p <- p + theme(legend.position="top") + labs(fill="")

p

Figure 5. Protein domain schematic of human MAP kinases.

Using bioMart with the Gene Ontology term for "MAP kinase activity", it is possible to draw multiple human MAP kinases using data from UniProt. Yellow circles denote phosphorylation sites.

Customizing the draw functions

Various customizations are possible:

1. Alter chain color and outline.
2. Change the labels to a custom list (but remember that the plots are drawn from the bottom up)
3. Change the size and color of the phosphorylation symbols.

These are illustrated with the script below which generates Figure 6.

data("five_rel_data")
p <- draw_canvas(five_rel_data)
p <- draw_chains(p, five_rel_data,
             fill = "lightsteelblue1",   # chain fill color
             outline = "grey",            # chain outline
             labels = c("p50/p105",      # custom protein names
                          "p50/p105",
                          "p52/p100",
                          "p52/p100",
                          "Rel B",
                          "c-Rel",
                          "p65/Rel A"),
             label_size = 5)
# change size and color of phosphorylation sites
p <- draw_phospho(p, five_rel_data, size = 3, fill = "red")
p + theme_bw()

Figure 6. Customizing the protein schematic of the NF-kappaB family.

Using arguments in the draw_chains() and draw_phospho() functions, it is possible customize colors and labels.

Discussion

After 20 years of manual drawing of protein schematics and experience in proteomic studies^14–17, the need for a more sustainable and programmatic method seemed important and worthwhile. It seemed wise to develop an approach that would integrate protein visualizations with other bioinformatic tools available in Bioconductor. This package represents an approach to enabling the reproducible and programmatic generation of protein schematics.

The plan is to develop this package further in terms of generating use cases and adding features. A list of issues for future development has already been added by the author on GitHub. Bug reports, feedback on desired features or code contributions can be made through GitHub.

The challenge with protein visualization is that specialist domain knowledge sometimes trumps databases. Thus, while integrating knowledge from UniProt is an excellent starting point, it is also essential to allow customisation of plots. This can be done by adding or removing information about the proteins, protein features and post-translational modification to the dataframe object made with R.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.

Software availability

The drawProteins package is available at: http://bioconductor.org/packages/drawProteins/

Source code is available at: https://github.com/brennanpincardiff/drawProteins

Archived source code as at time of publication: https://github.com/brennanpincardiff/drawProteins/tree/v1.0.2 and https://doi.org/10.5281/zenodo.1306619¹⁸

License: MIT+

Competing interests

No competing interests were disclosed.

Grant information

PB has been supported by funding from Bloodwise, UK.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements

Thanks to Steph Locke (https://github.com/stephlocke) and Dave Parr (https://github.com/DaveRGP) from the CaRdiff R User Group for help, advice and support that led to the development of this package. Without them the package just wouldn’t have happened.

Some of the Use Cases above have been shown on the authors blog, R for Biochemists (http://rforbiochemists.blogspot.co.uk/).

Faculty Opinions recommended

References

1. Ren J, Wen L, Gao X, et al.: DOG 1.0: illustrator of protein domain structures. Cell Res. 2009; 19(2): 271–3. PubMed Abstract | Publisher Full Text
2. Liu W, Xie Y, Ma J, et al.: IBS: an illustrator for the presentation and visualization of biological sequences. Bioinformatics. 2015; 31(20): 3359–3361. PubMed Abstract | Publisher Full Text | Free Full Text
3. Corpas M: The BioJS article collection of open source components for biological data visualisation [version 1; referees: not peer reviewed]. F1000Res. 2014; 3: 56. PubMed Abstract | Publisher Full Text | Free Full Text
4. Skidmore ZL, Wagner AH, Lesurf R, et al.: GenVisR: Genomic Visualizations in R. Bioinformatics. 2016; 32(19): 3012–3014. PubMed Abstract | Publisher Full Text | Free Full Text
5. The UniProt Consortium: Uniprot: the universal protein knowledgebase. Nucleic Acids Res. 2017; 45(D1): D158–D169. PubMed Abstract | Publisher Full Text | Free Full Text
6. Nightingale A, Antunes R, Alpi E, et al.: The Proteins API: accessing key integrated protein and genome information. Nucleic Acids Res. 2017; 45(W1): W539–W544. PubMed Abstract | Publisher Full Text | Free Full Text
7. Wickham H: ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009. Reference Source
8. Wilson G, Bryan J, Cranston K, et al.: Good enough practices in scientific computing. PLoS Comput Biol. 2017; 13(6): e1005510. PubMed Abstract | Publisher Full Text | Free Full Text
9. The uniprot proteins api. 2018. Reference Source
10. Lawrence T: The nuclear factor NF-kappaB pathway in inflammation. Cold Spring Harb Perspect Biol. 2009; 1(6): a001651. PubMed Abstract | Publisher Full Text | Free Full Text
11. Durinck S, Moreau Y, Kasprzyk A, et al.: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005; 21(16): 3439–3440. PubMed Abstract | Publisher Full Text
12. Durinck S, Spellman PT, Birney E, et al.: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009; 4(8): 1184–91. PubMed Abstract | Publisher Full Text | Free Full Text
13. Smith M, Durinck S, Huber W: The biomart users guide. 2017. Reference Source
14. Brennan P, Babbage JW, Thomas G, et al.: p70^s6k integrates phosphatidylinositol 3-kinase and rapamycin-regulated signals for E2F regulation in T lymphocytes. Mol Cell Biol. 1999; 19(7): 4729–4738. PubMed Abstract | Publisher Full Text | Free Full Text
15. Brennan P, Floettmann JE, Mehl A, et al.: Mechanism of action of a novel latent membrane protein-1 dominant negative. J Biol Chem. 2001; 276(2): 1195–1203. PubMed Abstract | Publisher Full Text
16. Fielding CA, Siebert S, Rowe M, et al.: Analysis of human tumour necrosis factor receptor 1 dominant-negative mutants reveals a major region controlling cell surface expression. FEBS Lett. 2004; 570(1–3): 138–142. PubMed Abstract | Publisher Full Text
17. Alsagaby SA, Khanna S, Hart KW, et al.: Proteomics-based strategies to identify proteins relevant to chronic lymphocytic leukemia. J Proteome Res. 2014; 13(11): 5051–5062. PubMed Abstract | Publisher Full Text
18. Brennan P: brennanpincardiff/drawProteins: F1000 publication release (Version v1.0.2). Zenodo. 2018. Data Source

Comments on this article Comments (1)

Version 1

VERSION 1 PUBLISHED 18 Jul 2018

Reader Comment 26 Jul 2018

Christopher Southan, TW2Informatics, Sweden

26 Jul 2018

Reader Comment

Does/can this tool parse @InterProDB coordinates?
Competing Interests: No competing interests were disclosed.
Does/can this tool parse @InterProDB coordinates?
Does/can this tool parse @InterProDB coordinates?
Competing Interests: No competing interests were disclosed. Close
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Author details Author details

Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, Wales, UK

Paul Brennan
Roles: Conceptualization, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

PB has been supported by funding from Bloodwise, UK.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

version 1

Published: 18 Jul 2018, 7:1105

https://doi.org/10.12688/f1000research.14541.1

© 2018 Brennan P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Brennan P. drawProteins: a Bioconductor/R package for reproducible and programmatic generation of protein schematics [version 1; peer review: 2 approved]. F1000Research 2018, 7:1105 (https://doi.org/10.12688/f1000research.14541.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Open Peer Review

Version 1

VERSION 1

PUBLISHED 18 Jul 2018

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Reviewer Report 22 Aug 2018

Malachi Griffith, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

Approved

https://doi.org/10.5256/f1000research.15828.r37075

The author describes the creation of a Bioconductor/R package for generic visualization of 2D protein diagrams. The tool does not address 3D visualizations or visualization of protein sequences or alignments. Rather it allows the user to view the position of protein annotations within an arbitrary number of proteins. By default, UniProt sequence identifiers and associated annotations are supported. The demonstrated examples depict protein domains/motifs and phosphorylation sites. Identifying a set of proteins to view from the Gene Ontology via bioMart is also demonstrated. There are likely to be many additional useful queries that could be achieved by similar bioMart queries.

The package leans on ggplot2 functionality for the actual drawing of each protein schematic. The drawings consist of simple elements: colored rectangles and circles that can be nested, overlaid, and labeled. A few simple use cases are illustrated. The writing is brief and straightforward. The code is available under an open access document.

I was able to install the package and run through the examples in the paper in a few minutes with only minor difficulties.

In particular, I had trouble getting this line to work in the example:
```
output <- getBM(attributes = c('uniprotswissprot', 'hgnc_symbol'), filters = 'go', values = 'GO:0004707', mart = ensembl)
```

Perhaps some problem with the way the single quotes are being encoded in that section?

Overall the tool is quite basic but also appears to be customizable. I anticipate it could be useful to many who wish to automate visualization of protein drawings and integrate these with visualization of various types omics data.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report 22 Aug 2018

Matej Lexa, Department of Machine Learning and Data Processing, Faculty of Informatics, Masaryk University, Botanicka 68a, 60200 Brno, Czech Republic

Approved

https://doi.org/10.5256/f1000research.15828.r37074

The paper describes a protein annotation visualization tool for R/Bioconductor that has already been in use for more than a year. The software is mature, I was able to install it easily, all the examples of code in the manuscript are functional. I only noticed a small deviation in the biomaRt example, most likely caused by slightly different data returned by biomaRt.

The manuscripts describes the purpose and functioning of the package in sufficient detail. However, I found the introduction a bit too brief, especially in terms of mentioning other similar software and advantages of drawProteins. For example, I found a Bioconductor package called Pviz that may be able to generate protein annotation visualizations.

As a minor remark, the last sentence in this paragraph on page 3 seems unclear to me:
"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."

Finally, a suggestion. The software would be even more useful if it could handle reading and writing of standard protein annotation files in GFF or similar format. UniProt wikipedia page describes how it is applied to proteins in this example: https://www.uniprot.org/uniprot/P0A7B8.gff Although GFF is much more popular with genomic data, it seems to be ocassionally used for annotating protein sequences.

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: bioinformatics, plant biology and genomics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response 31 Aug 2018

Paul Brennan, Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, UK

31 Aug 2018

Author Response

Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of ... Continue reading Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of the proteins.
I was inspired by your suggestion about GFF formats. I have created a gff_parser() function that is now available on Github and in the development version of drawProteins. It will be interesting to see if this is found useful. Interestingly for RelA/p65, the GFF format only includes annotation about the protein folding and not annotation about the domains. For that reason, I have also created a draw_folding() function which colours the regions of the protein according to STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
Thanks again,
Paul
----
Paul Brennan,
Cardiff University
Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of the proteins.
I was inspired by your suggestion about GFF formats. I have created a gff_parser() function that is now available on Github and in the development version of drawProteins. It will be interesting to see if this is found useful. Interestingly for RelA/p65, the GFF format only includes annotation about the protein folding and not annotation about the domains. For that reason, I have also created a draw_folding() function which colours the regions of the protein according to STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
Thanks again,
Paul
----
Paul Brennan,
Cardiff University
Competing Interests: I am the author of the manuscript and the drawProteins package. Close
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Author Response 31 Aug 2018

Paul Brennan, Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, UK

31 Aug 2018

Author Response

Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of ... Continue reading Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of the proteins.
I was inspired by your suggestion about GFF formats. I have created a gff_parser() function that is now available on Github and in the development version of drawProteins. It will be interesting to see if this is found useful. Interestingly for RelA/p65, the GFF format only includes annotation about the protein folding and not annotation about the domains. For that reason, I have also created a draw_folding() function which colours the regions of the protein according to STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
Thanks again,
Paul
----
Paul Brennan,
Cardiff University
Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of the proteins.
I was inspired by your suggestion about GFF formats. I have created a gff_parser() function that is now available on Github and in the development version of drawProteins. It will be interesting to see if this is found useful. Interestingly for RelA/p65, the GFF format only includes annotation about the protein folding and not annotation about the domains. For that reason, I have also created a draw_folding() function which colours the regions of the protein according to STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
Thanks again,
Paul
----
Paul Brennan,
Cardiff University
Competing Interests: I am the author of the manuscript and the drawProteins package. Close
Report a concern

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Version 1

VERSION 1 PUBLISHED 18 Jul 2018

Reader Comment 26 Jul 2018

Christopher Southan, TW2Informatics, Sweden

26 Jul 2018

Reader Comment

Does/can this tool parse @InterProDB coordinates?
Competing Interests: No competing interests were disclosed.
Does/can this tool parse @InterProDB coordinates?
Does/can this tool parse @InterProDB coordinates?
Competing Interests: No competing interests were disclosed. Close
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Version 1 18 Jul 18	read	read

Matej Lexa, Masaryk University, Botanicka 68a, 60200 Brno, Czech Republic
Malachi Griffith, Washington University School of Medicine, St. Louis, USA

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22 Aug 2018 | for Version 1

Malachi Griffith, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

11 Views Cite this report Responses(0)

Approved

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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24 Views

22 Aug 2018 | for Version 1

Matej Lexa, Department of Machine Learning and Data Processing, Faculty of Informatics, Masaryk University, Botanicka 68a, 60200 Brno, Czech Republic

24 Views Cite this report Responses(1)

Approved

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

bioinformatics, plant biology and genomics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response

31 Aug 2018

Paul Brennan, Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, UK

Dear Matej,
Thanks for your review.
I will look at extending the introduction over the next little while and also clarifying my remark about the order of the proteins.
I was inspired by your suggestion about GFF formats. I have created a gff_parser() function that is now available on Github and in the development version of drawProteins. It will be interesting to see if this is found useful. Interestingly for RelA/p65, the GFF format only includes annotation about the protein folding and not annotation about the domains. For that reason, I have also created a draw_folding() function which colours the regions of the protein according to STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
Thanks again,
Paul
----
Paul Brennan,
Cardiff University

View more View less

Competing Interests

I am the author of the manuscript and the drawProteins package.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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drawProteins: a Bioconductor/R package for reproducible and programmatic generation of protein schematics

Abstract

Keywords

Introduction

Methods

System requirements

Implementation

Operation

Figure 1. Protein domain schematic of RelA/p65.

Figure 2. Protein domain schematic of RelA/p65.

Use cases

Putting the RelA workflow together and adding titles

Figure 3. More detailed protein domain schematic of RelA/p65.

Drawing schematic for multiple proteins

Figure 4. Protein domain schematic of human NF-kappaB proteins.

Working with BioMart

Figure 5. Protein domain schematic of human MAP kinases.

Customizing the draw functions

Figure 6. Customizing the protein schematic of the NF-kappaB family.

Discussion

Data availability

Software availability

Competing interests

Grant information

Acknowledgements

References

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