Last updated: 2025-06-01

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Rmd	4e391c3	Maeva TECHER	2025-05-30	add new analysis orthology, synteny
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html	edb70fe	Maeva TECHER	2024-11-08	overlap and deg results created
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Rmd	564d596	Maeva A. TECHER	2024-05-14	wflow_publish("analysis/2_synteny-graphs.Rmd")
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html	f77543d	Maeva A. TECHER	2023-12-18	Build site.
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1. Load packages and prepare the R environment

These instructions are for installing GENESPACE within on the TAMU Grace cluster. While a new version of OrthoFinder/3.00 has been used for the gene orthology, GENESPACE works only with OrthoFinder/2.5.5, thus all downstream modules are loaded in regards to this.

1.To install GENESPACE itself, you first need to load R and specify path for local packages as described here

To run R without bothering other users, we will claim one interactive node to make sure we can proactively update the package if there are some issues:

srun --ntasks 1 --cpus-per-task 16 --mem 50G --time 05:00:00 --pty bash

We need to load the needed modules as follow

ml GCC/12.3.0 OpenMPI/4.1.5 R_tamu/4.3.2 OrthoFinder/2.5.5 MCScanX/2024.12.19
ml WebProxy # if you are on a core and not on the login node

export R_LIBS=$SCRATCH/R_LIBS_USER/4.3.2-foss-2023a:$R_LIBS

Once R is active, run the following command in the R terminal:

R # Launches R terminal 

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

install.packages("dbscan", type = "source", INSTALL_opts = "--no-test-load") # if some issues with GENESPACE install

devtools::install_github("jtlovell/GENESPACE", force = TRUE)

Verify that GENESPACE have been installed by running

library(GENESPACE)

If no error message appears, then GENESPACE has been installed correctly. After this step, you may exit the R terminal with the command

q()

2. Running with our data

The code provided below is meant to generate GENESPACE results from a previous OrthoFinder run. Slight modifications can be made to run OrthoFinder within the GENESPACE pipeline.

Create the genome repository

GENESPACE expects a directory called the genomeRepo with a specific file structure for each species as follows:

└── genomeRepo
    └── species_gene_id
        ├── species_gene_id_genomic_gff.gz
        └── species_gene_id_translated_cds.faa.gz

You must create a folder under the genomeRepo for each species you want to run GENESPACE on and be sure to name the folder the same as the gene id you assigned to the species in OrthoFinder!

Create a file called genespace-script.R and copy and paste the following code into it:

library(GENESPACE)

genomeRepo <- "/scratch/group/songlab/maeva/LocustsGenomeEvolution/Polyneoptera_FINAL/13_GENESPACE/genomeRepo"
wd <- "/scratch/group/songlab/maeva/LocustsGenomeEvolution/Polyneoptera_FINAL/13_GENESPACE/genespace"
path2mcscanx <- "/sw/eb/sw/MCScanX/2024.12.19-foss-2023a/bin/"

##########################  CHANGE TO YOUR GENOME NAMES  ####################### 

#genomes2run <- c("Lmigr", "Sgreg", "Scanc", "Samer", "Snite", "Spice", "Sscub", "Asimp", "Gbima", "Glong","Csecu", "Brsri", "Pamer")

#genomes2run <- c( "Sgreg", "Scanc", "Samer", "Snite", "Spice", "Sscub", "Asimp", "Csecu", "Brsri", "Pamer")

genomes2run <- c( "Sgreg", "Scanc", "Samer", "Snite", "Spice", "Sscub", "Lmigr")

################################################################################



parsedPaths <- parse_annotations(
  rawGenomeRepo = genomeRepo,
  genomeDirs = genomes2run,
  genomeIDs = genomes2run,
  presets = "ncbi",
  genespaceWd = wd)



gpar <- init_genespace(
  wd = wd,
  path2mcscanx = path2mcscanx,
  rawOrthofinderDir = "/scratch/group/songlab/maeva/LocustsGenomeEvolution/Polyneoptera_FINAL/5_OrthoFinder/fasta/Results_May26_iqtree",
  genomeIDs = genomes2run
) # Note: If you do not have a previous OrthoFinder run you want to run GENESPACE on, 
                                                    # just leave this option and only include the wd and path2mcscanx options and 
                                                    # GENESPACE will automatically run OrthoFinder for you

out <- run_genespace(gpar)
saveRDS(out, file = "genespace_output.rds")

Reload the session to customize the riparian plot

We are making plots below with original sense of the chromosome and reordered ones to see better the synteny. We also made some plots to highlight the inversion blocks. We used S. gregaria as reference for most of the synteny mapping and ordered the species to locusts on top and sedentary on the bottom.

out <- readRDS("genespace_output.rds")

# View available genome IDs and paths
out$genomes

# Visualize dotplot for two species
plot_dotplot(out, refGenome = "Sgreg", qryGenome = "Spice")

# Plot summary synteny blocks
plot_genomespace(out)

library(ggplot2)
ggthemes <- ggplot2::theme(
  panel.background = ggplot2::element_rect(fill = "white"))

customPal <- colorRampPalette(
  c("darkorange", "skyblue", "darkblue", "purple", "darkred", "salmon"))

customPal2 <- colorRampPalette(
  c("blue2", "darkblue"))


# Set output PDF file
pdf("riparian_plots.pdf", width = 10, height = 7)  # adjust width/height as needed

# Original chromosome sense from NCBI and order from GENESPACE
ripDat1 <- plot_riparian(
  gsParam = out, 
  palette = customPal,
  braidAlpha = .75,
  chrFill = "lightgrey",
  addThemes = ggthemes,
  refGenome = "Sgreg",
  chrLabFontSize = 10)

# Original chromosome sense from NCBI
ripDat2 <- plot_riparian(
  gsParam = out, 
  palette = customPal,
  invertTheseChrs = invchr,
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  braidAlpha = .75,
  chrFill = "lightgrey",
  addThemes = ggthemes,
  refGenome = "Sgreg",
  chrLabFontSize = 10)

# Chromosome rearranged
invchr <- data.frame(
  genome = c( "Snite", "Snite", "Spice", "Samer", "Scanc", "Sscub", "Scanc","Spice", "Sscub", "Snite", "Samer", "Sgreg", "Sscub", "Sscub", "Sgreg", "Samer", "Sgreg", "Spice", "Sscub", "Snite","Sscub", "Scanc", "Spice", "Spice", "Sscub"), 
  chr = c(1, 2, 2, 2, 3, 4, 5, 4, 5, 6, 5, 6, 8, 7, 8, 8, 9, 9, 10, 12, 12, 11, 11, "X", 3))

ripDat3 <- plot_riparian(
  gsParam = out, 
  minChrLen2plot = 10,
  invertTheseChrs = invchr,
  refGenome = "Sgreg",
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  chrLabFontSize = 7)


ripDat4 <- plot_riparian(
  gsParam = out, 
  palette = customPal,
  invertTheseChrs = invchr,
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  braidAlpha = .75,
  chrFill = "lightgrey",
  addThemes = ggthemes,
  refGenome = "Sgreg",
  chrLabFontSize = 10)

# Inversions highlighted

ripDat5 <- plot_riparian(
  gsParam = out, 
  palette = customPal,
  braidAlpha = .75,
  chrFill = "lightgrey",
  addThemes = ggthemes,
  refGenome = "Sgreg",
  chrLabFontSize = 10,
  inversionColor = "black")


ripDat6 <- plot_riparian(
  gsParam = out, 
  palette = customPal,
  invertTheseChrs = invchr,
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  braidAlpha = .75,
  chrFill = "lightgrey",
  addThemes = ggthemes,
  refGenome = "Sgreg",
  chrLabFontSize = 10,
  inversionColor = "black")



ripDat7 <- plot_riparian(
  gsParam = out, 
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  braidAlpha = .75,
  chrFill = "lightgrey",
  refGenome = "Sgreg",
  chrLabFontSize = 10, 
  addThemes = ggthemes,
  inversionColor = "darkred")

ripDat8 <- plot_riparian(
  gsParam = out, 
  invertTheseChrs = invchr,
  palette = customPal2,
  genomeIDs = c("Snite", "Sscub", "Samer","Scanc", "Spice", "Sgreg"), 
  braidAlpha = .75,
  chrFill = "lightgrey",
  refGenome = "Sgreg",
  chrLabFontSize = 10, 
  addThemes = ggthemes,
  inversionColor = "darkred")

dev.off()

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Asia/Tokyo
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       cli_3.6.5         knitr_1.49        rlang_1.1.6      
 [5] xfun_0.51         stringi_1.8.4     promises_1.3.2    jsonlite_1.9.1   
 [9] workflowr_1.7.1   glue_1.8.0        rprojroot_2.0.4   git2r_0.35.0     
[13] htmltools_0.5.8.1 httpuv_1.6.15     sass_0.4.9        rmarkdown_2.29   
[17] evaluate_1.0.3    jquerylib_0.1.4   tibble_3.2.1      fastmap_1.2.0    
[21] yaml_2.3.10       lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1    
[25] compiler_4.4.2    fs_1.6.5          Rcpp_1.0.14       pkgconfig_2.0.3  
[29] rstudioapi_0.17.1 later_1.4.1       digest_0.6.37     R6_2.6.1         
[33] pillar_1.10.2     magrittr_2.0.3    bslib_0.9.0       tools_4.4.2      
[37] cachem_1.1.0

Syntenic relations and graphs

Emily Baker, Maeva Techer

2025-06-01

1. Load packages and prepare the R environment

2. Running with our data