ggmsa: Plot multiple sequence alignment using ggplot2

Abstract

Plot multiple sequence alignment using ggplot2 with multiple color schemes supported.

Citation

To do

Introduction

Supports visualizing multiple sequence alignment of DNA and protein sequences using ggplot2 It supports a number of colour schemes, including Chemistry, Clustal, Shapely, Taylor and Zappo. Multiple sequence alignment can easily be combined with other ‘ggplot2’ plots, such as aligning a phylogenetic tree produced by ‘ggtree’ with multiple sequence alignment.

Installation

This R package (ggmsa, current version: 0.0.2) is avalable via CRAN. The way to install the package is the following:

## installing the package
install.packages("ggmsa")
## ## loading the package
library("ggmsa")

Load sample data

The file sample.fasta is shipped with the ggmsa package. To determine where the file is located, enter the following command in your R session:

sequences <- system.file("extdata", "sample.fasta", package = "ggmsa")
print(sequences)
#> [1] "/tmp/RtmpWSdJck/Rinst870267da3f6e7/ggmsa/extdata/sample.fasta"

Printing Multiple Sequence Alignments

We offer 5 color schemes for multiple sequence alignments.

Clustal X Colour Scheme

This is an emulation of the default colourscheme used for alignments in Clustal X, a graphical interface for the ClustalW multiple sequence alignment program. Each residue in the alignment is assigned a colour if the amino acid profile of the alignment at that position meets some minimum criteria specific for the residue type.

ggmsa(sequences, 320, 360, color = "Clustal")

Color by Chemistry

Amino acids are colored according to their side chain chemistry:

ggmsa(sequences, 320, 360, color = "Chemistry_AA")

Color by Shapely

This color scheme matches the RasMol amino acid and RasMol nucleotide color schemes, which are, in turn, based on Robert Fletterick’s “Shapely models”.

ggmsa(sequences, 320, 360, color = "Shapely_AA")

Color by Taylor

This color scheme is taken from Taylor(Taylor 1997) and is also used in JalView(Waterhouse et al. 2009).

ggmsa(sequences, 320, 360, color = "Taylor_AA")

Color by Zappo

This scheme colors residues according to their physico-chemical properties, and is also used in JalView(Waterhouse et al. 2009).

ggmsa(sequences, 320, 360, color = "Zappo_AA")

Do not print letters

If you specify font = NULL, only the background box will be printed.

ggmsa(sequences, 320, 360, font = NULL, color = "Chemistry_AA")

Combining plots

ggmsa is based on ggplot2, so combining multiple sequence alignment with other plots generated by ggplot2 is simple

Combining with tree

Cross-link with ggtree(Yu et al. 2017) package:

library(Biostrings)
x <- readAAStringSet(sequences)
d <- as.dist(stringDist(x, method = "hamming")/width(x)[1])
library(ape)
tree <- bionj(d)
library(ggtree)
p <- ggtree(tree ) + geom_tiplab()

data = tidy_msa(x, 164, 213)
p + geom_facet(geom = geom_msa, data = data,  panel = 'msa',
               font = NULL, color = "Chemistry_AA") +
    xlim_tree(1)

Combining with sequence logo

Cross-link with ggseqlogo(Wagih 2017) package. Bits of sequence letters can be indicated in the sequence logo.

f <- system.file("extdata", "LeaderRepeat_All.fa", package = "ggmsa")
s <- readDNAStringSet(f)
strings <- as.character(s)
p1 <- ggmsa(s, font = NULL, color = 'Chemistry_NT')

library(ggseqlogo)
library(cowplot)

p2 <- axis_canvas(p1, axis='x')+ geom_logo(strings, 'probability') 
pp <- insert_xaxis_grob(p1, p2, position="top", grid::unit(.05, "null"))
ggdraw(pp)

References

Taylor, W R. 1997. “Residual Colours: A Proposal for Aminochromography.” Protein Eng 10 (7): 743–46.

Wagih, Omar. 2017. “Ggseqlogo: A Versatile R Package for Drawing Sequence Logos.” Bioinformatics 33 (22).

Waterhouse, A. M., J. B. Procter, D. M. Martin, M Clamp, and G. J. Barton. 2009. “Jalview Version 2–a Multiple Sequence Alignment Editor and Analysis Workbench.” Bioinformatics 25 (9): 1189.

Yu, Guangchuang, David K Smith, Huachen Zhu, Yi Guan, and Tommy Tsanyuk Lam. 2017. “Ggtree: An R Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data.” Methods in Ecology and Evolution 8 (1): 28–36.