recDplot

recDplot is an R package for visualizing recombination in viral sequences. This package may be used both for general analysis of recombination along with phylogenetic compatibility matrices and for inspection of specific genome regions.

Contents

• Introduction
• Installation
• Usage
• Contact

Introduction

recDplot provides two approaches for recombination analysis - pairwise distance correspondence plots (PDC plots) and pairwise distance deviation matrices (PDD matrices).

PDC plots

PDC plots utilize the pairwise genetic distances in different genome regions. In the absence of recombination, the substitutions are accumulated proportionally, and the distances in two regions will follow a linear relationship.

If there are recombinant viruses in the dataset, the pairwise genetic distances between recombinant virus and its parents will diverge from the regression line.

PDD matrices

To assess the degree of correspondence between genetic distances in different genome regions, PDC plots can be further summarized as pairwise distance deviation matrices (PDD matrices). To build such matrix, the alignment of genomes is divided into windows, then for each window pairwise genetic distances are built. For each pair of regions we estimate the root mean square error of linear regression and visualize it as a heatmap. The higher RMSE, the more pairwise genetic distances deviate from regression line, that might be the consequence of recombination.

Installation

recDplot can be installed with the devtools package:

install.packages("devtools")
library(devtools)
install_github("v-julia/recDplot")

Requirements

recDplot was tested with R-3.4.3 and R-4.1.3 version.

Usage

Building PDD matrix:

library(recDplot)
library(colorRamps)
library(gplots)

aln = read.dna('path_to_alignment', format="fasta", as.character=TRUE)
aln[aln=='-'] <- NA

PDD_matrix = calc_PDDmatrix(dna_object=aln, step=50, window=600, method="pdist", modification="pairwise") #calculate PDD matrix
heatmap.2(as.matrix(PDD_matrix), Rowv = FALSE, Colv = "Rowv", dendrogram = 'none', col=matlab.like, main="PDD matrix", tracecol=NA) # visualize PDD matrix as a heatmap

Building PDC plot and its control

aln = read.dna('path_to_alignment', format="fasta", as.character=TRUE)
aln[aln=='-'] <- NA

list_control <-  plot_PDCP_control(aln, 1, 1000, 5000, 6000)
list_control[1] # ggplot with PDC control

head(list_control[[2]], n=5)  # table with pairwise distances between sequences in two regions in control alignment

#                            row                                 col   value.x   value.y
#1 AB039774_NA_NA_NA_GI.5_GI.P12       AB039774_NA_NA_NA_GI.5_GI.P12 0.0000000 0.0000000
#2 AB039774_NA_NA_NA_GI.5_GI.P12  AB042808_JPN_human_1987_GI.4_GI.P4 0.2201646 0.2476780
#3 AB039774_NA_NA_NA_GI.5_GI.P12     AB081723_JPN_NA_NA_GI.6_GI.PNA1 0.3023736 0.2898551
#4 AB039774_NA_NA_NA_GI.5_GI.P12 AB187514_JPN_human_1979_GI.3_GI.P14 0.2876289 0.3088843
#5 AB039774_NA_NA_NA_GI.5_GI.P12  AB435514_JPN_mouse_2005_GV.1_GV.P1 0.5139186 0.5128755

list_PDC <-  plot_PDCP(aln, 1, 1000, 5000, 6000)
list_PDC[1] # ggplot with PDC plot

head(list_PDC[[2]], n=5)  # table with pairwise distances between sequences in two regions
#                            row                                 col   value.x   value.y
#1 AB039774_NA_NA_NA_GI.5_GI.P12       AB039774_NA_NA_NA_GI.5_GI.P12 0.0000000 0.0000000
#2 AB039774_NA_NA_NA_GI.5_GI.P12  AB042808_JPN_human_1987_GI.4_GI.P4 0.2372529 0.2306122
#3 AB039774_NA_NA_NA_GI.5_GI.P12     AB081723_JPN_NA_NA_GI.6_GI.PNA1 0.3183246 0.2744898
#4 AB039774_NA_NA_NA_GI.5_GI.P12 AB187514_JPN_human_1979_GI.3_GI.P14 0.3100937 0.2865916
#5 AB039774_NA_NA_NA_GI.5_GI.P12  AB435514_JPN_mouse_2005_GV.1_GV.P1 0.5620112 0.4685891

To explore which sequence pairs distances deviate from regression line, we suggest to use 'plotly' package.

plot_ly(list_PDC[[2]],type="scatter", x = ~value.x,
        y = ~value.y, colors = "Set1", text = ~paste(row, '\n', col))%>%
  layout(xaxis = list(title = '1-1000'),
         yaxis = list(title = '5000-6000'), legend = list(title=list(text='Interactive PDC plot')))

PDC plot and its control can be visualized on one figure

list_PDCP_with_control <-  plot_PDCP_with_control(aln, 1, 500, 501, 1000)
list_PDCP_with_control[1] # ggplot with PDC and control in one figure

Web-version

recDplot functions are also available in shiny app http://v-julia.shinyapps.io/recdplot_app.

Contact

Your comments, bug reports, and suggestions are very welcomed. You can leave your comment via email: vjulia94@gmail.com