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https://thackl.github.io/gggenomes/

A grammar of graphics for comparative genomics
https://thackl.github.io/gggenomes/

biological-data comparative-genomics genomics-visualization ggplot-extension ggplot2

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A grammar of graphics for comparative genomics

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README 

        
# gggenomes 



## A grammar of graphics for comparative genomics



gggenomes is a versatile graphics package for comparative genomics. It extends the popular R visualization package [ggplot2](https://ggplot2.tidyverse.org/) by adding dedicated plot functions for genes, syntenic regions, etc. and verbs to manipulate the plot to, for example, quickly zoom in into gene neighborhoods.



## A realistic use case comparing six viral genomes



gggenomes makes it easy to combine data and annotations from different sources into one comprehensive and elegant plot. Here we compare the genomic architecture of 6 viral genomes initially described in [Hackl et al.: Endogenous virophages populate the genomes of a marine heterotrophic flagellate](http://dx.doi.org/10.1101/2020.11.30.404863)



![](man/figures/emales.png)



```R

library(gggenomes)



# to inspect the example data shipped with gggenomes

data(package="gggenomes")



gggenomes(

  genes = emale_genes, seqs = emale_seqs, links = emale_ava,

  feats = list(emale_tirs, ngaros=emale_ngaros, gc=emale_gc)) |> 

  add_sublinks(emale_prot_ava) |>

  sync() + # synchronize genome directions based on links

  geom_feat(position="identity", size=6) +

  geom_seq() +

  geom_link(data=links(2)) +

  geom_bin_label() +

  geom_gene(aes(fill=name)) +

  geom_gene_tag(aes(label=name), nudge_y=0.1, check_overlap = TRUE) +

  geom_feat(data=feats(ngaros), alpha=.3, size=10, position="identity") +

  geom_feat_note(aes(label="Ngaro-transposon"), data=feats(ngaros),

      nudge_y=.1, vjust=0) +

  geom_wiggle(aes(z=score, linetype="GC-content"), feats(gc),

      fill="lavenderblush4", position=position_nudge(y=-.2), height = .2) +

  scale_fill_brewer("Genes", palette="Dark2", na.value="cornsilk3")

  

ggsave("emales.png", width=8, height=4)

```



For a reproducible recipe describing the full *evolution* of an earlier version of this plot with an older version of gggenomes starting from a mere set of contigs, and including the bioinformatics analysis workflow, have a look at [From a few sequences to a complex map in

minutes](https://thackl.github.io/gggenomes/articles/emales.html).



## Motivation & concept



Visualization is a corner stone of both exploratory analysis and science

communication. Bioinformatics workflows, unfortunately, tend to generate a

plethora of data products often in adventurous formats making it quite difficult

to integrate and co-visualize the results. Instead of trying to cater to the all

these different formats explicitly, gggenomes embraces the simple

tidyverse-inspired credo:



- Any data set can be transformed into one (or a few) tidy data tables

- Any data set in a tidy data table can be easily and elegantly visualized



As a result gggenomes helps bridge the gap between data generation, visual

exploration, interpretation and communication, thereby accelerating

biological research.



Under the hood gggenomes uses a light-weight track system to accommodate a mix

of related data sets, essentially implementing ggplot2 with multiple tidy tables

instead of just one. The data in the different tables are tied together through

a global genome layout that is automatically computed from the input and defines

the positions of genomic sequences (chromosome/contigs) and their associated

features in the plot.



## Inspiration



gggenomes draws inspiration from some brilliant packages, in particular:

* [gggenes](https://github.com/wilkox/gggenes) by [David Wilkins](https://wilkox.org/)

* [ggtree](https://guangchuangyu.github.io/software/ggtree/) by Guangchuang Yu

* [ggraph](https://github.com/thomasp85/ggraph) by [Thomas Lin Pedersen](https://www.data-imaginist.com/about)



## Installation



gggenomes is at this point in an alpha release state, and only

available as a developmental package from github.



```R

# if you don't have it

install.packages("devtools") 



# install gggenomes

devtools::install_github("thackl/gggenomes")



# optionally install ggtree to plot genomes next to trees

# https://bioconductor.org/packages/release/bioc/html/ggtree.html

if (!requireNamespace("BiocManager", quietly = TRUE))

    install.packages("BiocManager")

BiocManager::install("ggtree")

```