Assessing synteny identification
Fabricio Almeida-Silva
VIB-UGent Center for Plant Systems Biology, Ghent University, Ghent, BelgiumYves Van de Peer
VIB-UGent Center for Plant Systems Biology, Ghent University, Ghent, BelgiumSource:
vignettes/vignette_03_assessing_synteny.Rmd
vignette_03_assessing_synteny.Rmd
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. Zhao and Schranz (2019) proposed a network-based synteny analysis (algorithm now implemented in the Bioconductor package 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:
Data description
Here, we will use a subset of the synteny network inferred in Zhao and Schranz (2019) that contains the synteny network for Brassica oleraceae, B. napus, and B. rapa.
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)
#> CC Node_count Rsquared Score
#> 1 0.877912 149144 0.6806854 89125.76
Ideally, you should infer synteny networks using 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
#> CC Node_count Rsquared Score Network
#> 1 0.8779120 149144 0.6806854 89125.76 net1
#> 2 0.8769428 149133 0.6813367 89105.97 net2
#> 3 0.8758974 149114 0.6810978 88957.20 net3
# Determine the best network
synnet_assesment$Network[which.max(synnet_assesment$Score)]
#> [1] "net1"
As you can see, the first (original) network is the best, as it has the highest score.
Session information
This document was created under the following conditions:
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