1. Introduction

1.1 Pan-genome and PAV analysis

  Pan-genome is the collective whole-genome sequences of a given population, revealing the diversity and functional potential within that population. The PAV(Presence/absence variation) analysis is an essential step in pan-genome analysis. The core genome contains genomic regions shared by all individuals and the distributed genome is not shared by all. The distributed genome can be further divided into genomic regions shared in most members (soft-core genome), regions shared between some members (distributed/accessory genome), and regions present in only one member (unique/private genome).

1.2 The functions in APAVplot

  APAVplot is a R package designed for APAV for the subsequent analysis and visualization of PAV profile. It is efficient to explore and visualize the complex results in PAV analysis. It provides the following modules:

• Visualization of coverage : First you need to build a COV class using get_cov_obj(). cov_heatmap() shows coverage profile in a heat map. cov_density() shows coverage distribution of interested regions.

• PAV statistics and analysis : First you need to build a PAV class using get_pav_obj(). pav_heatmap() shows PAV heat map. pav_hist(), pav_halfviolin() and pav_stackbar() present the basic statistics. pav_cluster() clusters the samples based on PAV table. pav_pca() do the PCA analysis.

• Phenotype association and visualization : pheno_stat performs phenotype association calculations. pheno_heatmap(), pheno_manhattan(), pheno_block(), pheno_bar() and pheno_violin() display the results.

• Drawing growth curve : sim_plot() is used for visualization of pan/core/private genome size estimation by simulation. The input is the output table of apav sim.

• Visualization of elements : plot_ele_cov(), plot_ele_pav() and plot_ele_depth() are used to check elements at coverage, PAV and depth level.

2. Installation

2.1 Installing R/RStudio

  In order to run APAVplot, we need the R software environment, which can be freely downloaded as follows:

• Install R
• Install RStudio

2.2 Check or install packages

packages <- c("data.table", "ggdendro", "ggplot2", "ggrepel", "ggsignif", "ggnewscale", "patchwork", "snowfall", "circlize")
lapply(packages, function(x) {
	if(!require(x, character.only = TRUE)) {
		install.packages(x, dependencies = TRUE)
	}})
if (!requireNamespace("BiocManager", quietly = TRUE)){
  install.packages("BiocManager")
}
BiocManager::install("ComplexHeatmap")

2.3 Install metaFunc from github.

if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")
library(devtools)
install_github("SJTU-CGM/APAVplot", build_vignettes = TRUE)