Supplementary Information of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"

<p><strong>Supplementary Figures</strong></p> <p><strong>Figure S1</strong> Population genetic statistics calculated in non-overlapping 10 Kb windows along the 14 chromosomes in the sea squirt genome.<br> <strong>Figure S2</strong> <em>C. robusta</em> introgression into <em>C. intestinalis</em> shown across the 14 chromosomes.<br> <strong>Figure S3</strong> Population genetic statistics of the <em>C. robusta</em> introgressed coding sequences.<br> <strong>Figure S4 </strong>ABBA-BABA introgression patterns using<em> C. edwardsi </em>as an outgroup.<br> <strong>Figure S5</strong> Inference of the divergence history between <em>C. robusta</em> and <em>C. intestinalis</em> with moments.<br> <strong>Figure S6 </strong>Selection tests.<br> <strong>Figure S7 </strong><em>C. robusta</em> ancestry along chromosome 5 in <em>C. intestinalis</em> individuals.<br> <strong>Figure S8</strong> Neighbor-joining trees of 50 Kb windows framing the &ldquo;missing data region&rdquo; (grey band) at the center of the chromosome 5 hotspot.<br> <strong>Figure S9</strong> Copy number variation at candidate SNPs in the introgression hotspot on chromosome 5 (700 Kb - 1.5 Mb).<br> <strong>Figure S10</strong> Structural analysis of the &ldquo;missing data region&rdquo; on chromosome 5 (from 1,009,000 to 1,055,000 bp).</p> <p>&nbsp;</p> <p><strong>Supplementary Tables</strong></p> <p><strong>Table S1</strong> Sample information.<br> <strong>Table S2 </strong>Correlation between chromosomes of the individual <em>C. robusta </em>ancestry fraction.<br> <strong>Table S3</strong> Demographic results with moments &ndash; excluding chromosome 5.<br> <strong>Table S4</strong> Demographic results with moments &ndash; including chromosome 5.<br> <strong>Table S5 </strong>Description of the Supplementary Data.</p> <p>&nbsp;</p> <p><strong>Supplementary Scripts</strong></p> <p><em>Bioinformatic pipeline used for genotyping and haplotyping.</em></p> <p><strong>Script #1</strong>: prepare the reference genome for BWA and GATK.<br> reference_bwa_GATK_CF.sh<br> <strong>Script #2</strong>: mapping the reads to the reference with BWA.<br> mapping_bwa-mem_CF.sh<br> <strong>Script #3</strong>: indel realignment with GATK.<br> indel_realignment_CF.sh<br> <strong>Script #4</strong>: individual variant calling in gVCF format with GATK.<br> snpindel_callingGVCF_raw_CF.sh<br> <strong>Script #5</strong>: joint genotyping with GATK.<br> joint_genotyping_raw_CF.sh<br> <strong>Script #6</strong>: genotype refinement with GATK.<br> genotype_refinement_raw_CF.sh<br> <strong>Script #7</strong>: SNPs and indels recalibration with GATK.<br> snpindel_recalibration_CF.sh<br> <strong>Script #8</strong>: genotype refinement after recalibration with GATK.<br> genotype_refinement_recal_CF.sh<br> <strong>Script #9</strong>: genotype correction.<br> phase_by_transmission_correctCalling_CF@2020.sh<br> <strong>Script #10</strong>: phasing with GATK and BEAGLE.<br> phase_by_transmission_clean_CF@2020.sh</p> <p><em>Pipeline used for the demographic inferences with moments.</em></p> <p><strong>Script #11</strong>: define the demographic models.<br> moments_models_2pop_bb_parallel_folded_2periods.py<br> <strong>Script #12</strong>: run the demographic inferences.<br> moments_inference_dualanneal_bb_parallel_folded_2periods_bounds.py</p>