Custom code used in the paper: Hybridization and introgression drives genome evolution of the Dutch Elm Disease pathogens

Open source software

All open source software used in the study is listed in Open_source_software.md

Bash commands

Bash commands used to run the analyses are in the file Bash_commands.md

Custom code

All custom scripts are organized in four folders according to the appearance in the Results section

01_Population_structure_and_cryptic_lineages:

DAPC_analyses

• plotDAPC_SNP.R # plotting DAPC analysis based on SNP variants
• plotDAPC_CNV.R # plotting DAPC analysis based on CNV variants

Figure1_plots

• simulate_point_locations.R # simulating coordinates of point labels
• map_geo.R # plotting a map with strains
• plotGlobe.R # plotting picture of a globe
• plotTree.py # plotting NJ phylogeny
• plotStructure.R # plotting structure analysis
• PCA_Plot.R # plotting PCA results

Mitochondrial_DNA

• writeVcfToFasta.py # conversion of vcf to fasta
• drawTree.py # plotting mtDNA tree

Structure_and_map_O_novo_ulmi

• simulate_point_locations.R # simulating coordinates of point labels
• plotStructureMap.R # plotting structure results for K = 3 on a map
• plotStructure_novoulmi.R # plotting structure barplots in O. novo-ulmi
• plotStructure_ulmi.R # plotting structure barplots in O. ulmi
• pca_ulm_plot.R # plotting PCA based on O. ulmi SNPs

02_Diversity_and_genomic_signatures_of_introgression

Comparing_introgression_events

• plotPCA.R # Plotting PCA for each introgressed region
• selectStrainsForBranchEstimationBootstrapAllOU.ipynb # Estimating branch length per each IR and bootstrapping estimates
• plotBranchEstimates.R # Plotting bootstrap values of branch length of each introgressed region
• calcStats.ipynb # Comparing and plotting branch lengths between rare and frequent IRs

Diversity_and_genotype_heatmap

• combineDxy.py # getting Dxy per window estimates
• convert2MajorMinor.py # converting genotypes into major-minor allele
• plotGenotypesHeatMap.R # plottig heatmap of genotypes
• plotDiversityDist.R # plotting distribution of diversity in 50 kb windows
• calculateCorrelations.R # plotting diversity/divergence correlations

Diversity_plots_in_windows

• plotAngsdWindows_short_Pi_Dxy.R # plotting tree weighting, Pi and Dxy, in windows across the genome
• plotAngsdWindows_short_Fst.R # plotting tree weighting and Fst in windows across the genome
• plotAngsdWindows_short_Taj.R # plotting tree weighting, Tajima's D and Dxy in windows across the genome
• plotGeneDiscordancies.R # plotting tree weighting in 100 SNP windows, filtered 100 SNP windows and 50 kb windows

Figure2_plots

• plotBoxplots.R # plotting boxplots of global and per-window distances and diversity in Ophiostoma lineages
• plot_Dstatistic.R # dotplot with standard errors of D-statistic
• mk_circos.sh # script for plotting circos chronogram

Figure3_plots

• plotTargetsAndDiversity.R # plotting ULM ancestry, diversity within, divergence between lineages and targets of positive selection across the genome

Gene_flow_between_Ophiostoma_lineages

• runUnbiasedD_Outgr.py # estimating D-statistic

High_diversity_regions

• bootstrapPi.py # detecting high diversity regions across the genome

MAT_loci

• plotDepth_heatmap.R # plotting coverage and admixture around mating types
• plotTree_MAT.py # plotting phylogenetic trees around mating types

PCA_in_windows

• runPCA.R # running PCA analysis for a window and extracting distance of each strain with ULM

Structure_in_windows_plots

• mk_circos.sh # script for plotting circos chronogram with structure in windows results

03_Genetic_basis_of_lineage_divergence

Identification_ULM_ancestry

• filter_ULMLike.py # Identifying strains with ULM ancestry from tree topology
• filterFasta.py # selecting strains from fasta alignments

Selection_test

• SnIPRE_running_script_Ophiostoma.R # running snipre program to detect genes under selection
• plotSnipreResults.R # plotting snipre results

Genetic_diversity_and_divergence

• diversity_divergence_stats_popgenome.R # calculating diversity/divergence in genes

04_Phenotypic_differences_between_lineages

Processing pictures and extracting growth information

• ophiostoma_phenotyping_picture_analysis_function.R # a function to process images
• ophiostoma_phenotyping_launch.R # launching a function for each picture

Modelling and plotting isolate growth in different conditions and virulence on apples

• growth_virulence_modeling_Fig4_S17_S18_script.R # plotting growth & virulence results