In almeidasilvaf/cogeqc: Systematic quality checks on comparative genomics analyses

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL
)

Introduction

Synteny analysis allows the identification of conserved gene content and gene order (collinearity) in a genomic segment, and it is often used to study how genomic rearrangements have shaped genomes during the course of evolution. However, accurate detection of syntenic blocks is highly dependent on parameters such as minimum number of anchors, and maximum number of upstream and downstream genes to search for syntenic blocks. @zhao2019network proposed a network-based synteny analysis (algorithm now implemented in the Bioconductor package r BiocStyle::Biocpkg("syntenet")) that allows the identification of optimal parameters using the network's average clustering coefficient and number of nodes. Here, we slightly modified the approach to also take into account how well the network's degree distribution fits a scale-free topology, which is a typical property of biological networks. This method allows users to identify the best combination of parameters for synteny detection and synteny network inference.

Installation

To install the package from Bioconductor, use the following code:

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cogeqc")

Loading the package after installtion:

# Load package after installation
library(cogeqc)
set.seed(123) # for reproducibility

Data description

Here, we will use a subset of the synteny network inferred in @zhao2019network that contains the synteny network for Brassica oleraceae, B. napus, and B. rapa.

# Load synteny network for 
data(synnet)

head(synnet)

Network-based assessment of synteny identification

To assess synteny detection, we calculate a synteny network score as follows:

$$ \begin{aligned} Score &= C N R^2_{SFT} \end{aligned} $$

where $C$ is the network's clustering coefficient, $N$ is the number of nodes, and $R^2_{SFT}$ is the coefficient of determination for the scale-free topology fit.

The network with the highest score is considered the most accurate. To score a network, you will use the function assess_synnet().

assess_synnet(synnet)

Ideally, you should infer synteny networks using r BiocStyle::Biocpkg("syntenet") with multiple combinations of parameters and assess each network to pick the best. To demonstrate it, let's simulate different networks through resampling and calculate scores for each of them with the wrapper function assess_synnet_list().

# Simulate networks
net1 <- synnet
net2 <- synnet[-sample(1:10000, 500), ]
net3 <- synnet[-sample(1:10000, 1000), ]
synnet_list <- list(
  net1 = net1, 
  net2 = net2, 
  net3 = net3
)

# Assess original network + 2 simulations
synnet_assesment <- assess_synnet_list(synnet_list)
synnet_assesment

# Determine the best network
synnet_assesment$Network[which.max(synnet_assesment$Score)]