Description of the data and file structure

There are two zipped files here: one contains all the codes for this analysis, and the other is the VCF file of SV and SNP calling used for this analysis.

File: Code.zip

All scripts were put in the file “src”.

Part 1: pan-genome analyses

1.1. assembly.sh

1) For the reference individual

2) For the other pan-genome individual

1.2. repeat elements.sh

1) For the reference individual

2) For the other pan-genome individual

1.3. annotations.sh

1.4. Pangenome_graph.sh

1) RUN PBSV for SV calling

2) RUN SVIM for SV calling

3) RUN Sniffles for SV calling

4) RUN cuteSV for SV calling

5) RUN SVIMasm for SV calling

6) RUN Assemblytics for SV calling

7) RUN merge SVs for SV calling

8) RUN winnowmap for SNP calling

9) RUN deepvariant for indel calling

10) Construct the  Pan-genome Graph

1.5 Collinearity Analyses.sh

1) Collinearity among Q. variabilis genome assemblies

2) Variation in synteny diversity across the genome

3) Synteny relationship index (SRI) between each pair of Q. variabilis genomes.

1.6 SV calling.sh

1) Genotyping SV in each sample of 257 re-seq individuals

2) Merge vcf

3) Filtering low-quality sv

Part 2: Population genetic analyses

2.1 SNP_calling_filter.sh

1) Build Indexes

2) Filter the raw data

3) Mapping 

4) SNP calling using GATK

5) SNP filtering 

2.2 population_structure.sh

1) Input prepare

2) PCANGsd

3) NGSadmix

4) NJ tree

2.3 summary_statistics.sh

1) Calculate the number of valid sites

2) Calculate tP, Tajima's D

3) Calculate Fay & Wu's H

4) Calculate ZnS

5) Calculate Fst

6) Calculate dxy

7) Calculate RND  

2.4 demography.sh

1) Perform fsc26

2.5 local_adaptation.sh

1) GF

2) LFMM2

3) RDA

4) Pcadapt

2.6 selective_sweep.sh

Perform XP-CLR

2.7 co-directional_SNPs.sh

Identification of co-directional and anti-directional SNPs of 323 shared outlier SNP

2.8 introgression.sh

 1) Conduct ML tree

 2) Perform ELAI

 3) Calculate fdm

 4) Calculate df

File: VCF_data.tar

The VCF files with SNP and SV calls for a total of 257 individuals across three oak species.

Dataset Overview:

——————VCF_data.tar

————SNPs_data

——Chr*a.mac.recode.vcf.gz

————SVs_data

——Qv_sv_minQ.mis.recode.vcf.gz

The Chr*a.mac.recode.vcf.gz files represent the SNP calls for each chromosome generated by GATK. It comprises 21,695,563 SNPs.

The Qv_sv_minQ.mis.recode.vcf.gz file represents the SV calls genome-wide generated by vg. It comprises 20,072,814 SVs.

File Details:

• File Name: Chr*a.mac.recode.vcf/Qv_sv_minQ.mis.recode.vcf
• File Format: VCF (Variant Call Format) Version 4.2
• Date: October 7, 2024
• Source Software: GATK /vg

Data Description:

The VCF file includes the following columns standard to the format:

• #CHROM: Chromosome number
• POS: Position of the SNP/SV on the chromosome
• ID: Identifier of the SNP/SV
• REF: Reference base
• ALT: Alternate base(s)
• QUAL: Quality score of the SNP/SV
• FILTER: Filter status
• INFO: Additional information (e.g., allele frequency, number of samples)
• FORMAT: Data format
• Sample columns: One per individual, containing genotype information

Specific Fields in INFO

• NS: Number of samples with data
• AF: Allele frequency
• DP: Total depth of reads

Specific Fields in FORMAT

• GT: Genotype
• AD: Allele depth
• DP: Read depth
• GQ: Genotype quality
• GL: Genotype likelihood

Access information

Other publicly accessible locations of the data:

• All sequencing data from this study have been deposited at NCBI Sequence Read Archive under Bioproject accession numbers SAMN38056469-SAMN38056477 and SAMN38043932-SAMN38044038.