hde08/Aspat_WesternPacific_Popgen: Aspat Population Genomics scripts

Analysis of Acropora spathulata population genomics across the Western Pacific DOI : https://doi.org/10.1016/j.cub.2026.04.057 The authors would appreciate being notified if you intend to use these data or analyses in your own work Title: Contrasting population structures of reef-building corals and their algal symbionts inform adaptive potential across the Western Pacific Reference : Current Biology 36, 1-15, e1-e8, June 8, 2026 Authors Hugo Denis1,2,3, Katharine E. Prata4,5, Hisatake Ishida6, Iva Popovic4,5, Véronique J.L. Mocellin4, Magali Boussion1, Ilha Byrne5, Steven W. Purcell3, Line K. Bay4, Gaël Lecellier7, Cheong Xin Chan6, Cynthia Riginos4,5, Emily J. Howells3,8†, Véronique Berteaux-Lecellier1,† 1 Santé des Ecosystèmes tropicaux (SantEco) EMR9001, CNRS, IRD, Univ. de Nouvelle-Calédonie, Univ. la Reunion, UMR 250 ENTROPIE, Noumea 98800, New Caledonia, France.2 ED 129, SU Sorbonne Université, 4, Place Jussieu, 75252 Paris, France3 National Marine Science Centre, Faculty of Science and Engineering, Southern Cross University, Coffs Harbour, NSW, Australia4 Australian Institute of Marine Science, Townsville, QLD, Australia5 School of the Environment, University of Queensland, Brisbane, QLD 4072, Australia6 Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia7 Institut de Sciences Exactes et Appliquées (ISEA) EA7484, 145, Avenue James Cook – BP R4 98 851 Nouméa, New Caledonia8 Lizard Island Research Station, Australian Museum, Sydney, NSW 2010, Australia†: These authors contributed equally to this work Correspondence : Hugo DENIS, Institut de Recherche pour le Développement, Promenade Roger-Laroque, Noumea cedex, New Caledonia, France, Email : denis.hugo08@gmail.com ORCID : https://orcid.org/0000-0003-2909-6299 WGS Data Processing & Analyses Scripts 1. Fastq_to_bam Modified from Iva Popovic custom scripts : https://github.com/ivapops/fastq_toBam Scripts format for execution using Slurm workload manager 1 Fastqc_multiqc.sh > Run initial quality check on raw sequencing data.2 Trimmomatic_slurm.sh & Trimmomatic_2nd_round_slurm.sh > Quality trimming and adapter removal.3 Bwa_mem_slurm_v2.sh.sh > Alignment to reference genome.4 picard_slurm.sh > Add read groups, mark and remove duplicates.5 Extract_bam_unmapped_slurm.sh > Extract unmapped reads in different bam file.6 BAM_statistics_slurm.sh > Generate statistics on raw sequence, quality, mapping, coverage etc. 2. Bam_to_vcf Script for variant 'hard calling' Following GATK 'Best practices' https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels 1 GATK_HaplotypeCaller_slurm.sh > per sample variant calling (using complete reference chromosomes + scaffolds)2 GATK_GenomicsDBimport_slurm.sh > Create genomic database (chromosomes only, split by 4 or 2 intervals depending on size)3 GATK_GenotypeGVCFs_slurm.sh > joint genotyping across all samples (per intervals)4 GATK_GatherVCFs_SelectVariants_slurm.sh > Combine intervals vcf files and select variants5 VariantFiltration_slurm.sh > Filter final vcf by variants, genotypes and individuals 3. Popgen_angsd Scripts to have a preliminary look at the data using a genotype likelihood framework 1 ANGSD_slurm.sh > Generate beagle file using ANGSD software2 PCangsd_slurm.sh > PCA and ADMIXTURE analyses using pcangsd3 IBS_slurm.sh > Compute Identity by state matrix in ANGSD 4. Popgen_vcf Scripts to conduct final population genetics analyses presented in the manuscript using 'hard called' variants. 1 Compare_sequencing_batch.sh > Confirm absence of strong batch effects 2 ADMIXTURE_slurm.sh > Perform ADMIXTURE analyses on filtered vcf file3 Stats_pixy_slurm.sh > Compute Fst, Dxy, Pi in PIXY4 RAxML_slurm.sh > Compute ML-tree in RAxML5 Original scripts to perform dadi analyses can be found at https://github.com/kepra3/kp_dadi/tree/ahya-demo 5. Symbionts Scripts to analyze symbiont communities composition and diversity from hologenome reads. Based on original scripts from Hisatake Ishida available at : https://github.com/hisatakeishida/Symb-SHIN 1 Preprocess_reads.sh > Separate Symbiodiniaceae reads from host reads2 Kmer_symbionts.sh > Generate k-mer counts per sample and compute D2S-derived distance3 GraftM_ITS2_slurm.sh > Recover ITS2 reads in hologenome data using sequence classifier tool GraftM4 psbAncr.sh > Map symbiont reads to psbAncr reference database and use uniquely mapped reads as relative abundance5 Symbionts_symportal_hologenome_comparison.Rmd > Compare Symbiodiniaceae diversity inferred from hologenome-based methods and ITS2 amplicon sequencing (Figure S8)6 Symbiont_NJ_tree_multi_markers_barplots.Rmd > Make NJ-tree based on D2S-derived distance and marker genes barplots (Figure S9a) 6. Stats_Plots Scripts to conduct statistical analyses and create main plots from the publication 1 Host_structure_plot.Rmd > Make Figure 12 IbD_Ne_plots.Rmd > Make Figure 23 Host_symbiont_tanglegram.Rmd > Make Figure 34 Symbiont_structure_plot.Rmd > Make Figure 45 RDA_analyses.Rmd > Scripts to conduct RDA analyses and produce results from Table 2