DOI: 10.5061/dryad.gf1vhhmvz
title: Data for: ‘Drifting’ Buchnera genomes track the microevolutionary trajectories of their aphid hosts

description: This repository comprises code and scripts needed to recreate the analyses
 presented in the manuscript: Thia et al. 'Drifting’ Buchnera genomes
 track the microevolutionary trajectories of their aphid hosts. 
 This study explores the coevolution between the endosymbiont Buchnera and
 its aphid host in the aphid species, Myzus persicae, the green peach
 aphid. Evidence that Buchnera is polymorphic within aphid clones indicates
 that these symbionts exist as populations of different strains within a
 single aphid. Strong signals of genomic covariation were observed between
 both organisms. However, genomic variation in either organism was not
 associated with geography or the host plant. Further examination of
 protein-coding SNPs in Buchnera found that the number of non-synonymous to
 synonymous mutations were on average comparable (ratio ~1), with similar
 site frequency spectra of alleles in both mutation classes. Our results
 suggest that Buchnera within Myzus persicae are largely evolving
 under neutral processes. This study provides a unique population genetics
 perspective of Buchnera-aphid host coevolution on a microevolutionary
 scale.
description: This study leverages previously and newly sequenced
 <em>Myzus persicae</em> to compile a global dataset of
 <em>Buchnera</em> and aphid host genomes. Analyses include
 quantification of genetic diversity and differentiation, protein-coding
 effects, and comparisons of site frequency spectra.
 More details can be found in the README.
description: # Data for: ‘Drifting’ *Buchnera* genomes track the microevolutionary
 trajectories of their aphid hosts This repository contains the data and
 scripts needed to replicate the analysis in: * Thia, Zhan, et al. (2024)
 ‘Drifting’ *Buchnera* genomes track the microevolutionary trajectories of
 their aphid hosts. *Insect Molecular Biology.* DOI:
 [10.1111/imb.12946](https://doi.org/10.1111/imb.12946) This study
 investigates the coevolution between *Buchnera* and its aphid host among a
 globally distributed sample of *Myzus persicae*. This repository is split
 into two ZIP files: ‘Data’ and ‘Scripts’. ‘Data’ contains two directories
 that set out the structure for these analyses: * 0_Metadata/ *
 1_Bioinformatics/ * 2_Popgen_analysis/ ‘Scripts’ contains the scripts that
 should be placed into either ‘1_Bioinformatics/’ or ‘2_Popgen_analysis/’
 to run the analyses for these separate pipelines. Note that the
 ‘2_Popgen_analysis/’ directory is intentionally left empty. This is meant
 to act as a placeholder to outline how the analysis pipeline should be
 structured. Note that in all tabulated files (e.g., CSV or XLSX), missing
 data is represented by blank cells. ## Code/Software This project used a
 combination of bioinformatics to derive genetic variants for *Myzus
 persicae* and its endosymbiotic bacteria *Buchnera*. These were then
 imported into R for population genetic analysis. Data structures and
 scripts are described below. ## Description of the data and file structure
 This repository is divided into three core directories: * 0_Metadata/ *
 1_Bioinformatics/ * 2_Popgen_analysis/ These directories should all
 coexist in the same main directory, as the R code for analyses directly
 refers to this structure. Bioinformatic bash scripts were executed on the
 University of Melbourne's Spartan HPC. These scripts should only be
 launched within their respective directories. See the
 'Repository_description.pdf' file. ## Data: 0\_Metadata/ This
 directory contains higher-level metadata for samples.
 **distance_coeff_names.csv** A table of labels to rename coefficients in
 linear models of genetic distance. | *Column name* | *Type* |
 *Description* | | :------------ | :-------- |
 :---------------------------------------------------- | | Coeff.Default |
 Character | Default coefficient name produced from the model in R | |
 Coeff.Rename | Character | New coefficient name to relabel |
 **metadata_combined.xlsx** Metadata for samples used in this study. |
 *Column name* | *Type* | *Description* | | :-------------- | :-------- |
 :----------------------------------- | | Sample | Character | Sample ID |
 | Continent | Character | Continent of origin | | Country | Character |
 Country of origin | | Locality | Character | Locality of origin | |
 Year\_collected | Character | Year collected | | Crop | Character | Crop
 of origin | | Plant\_family | Character | Plant family of origin | |
 Source | Character | The study from which the sample came | | Msat |
 Character | Microsatellite genotype | | Lat | Numeric | Latitude | | Long
 | Numeric | Longitude | | Notes | Character | Additional notes | ## Data:
 1\_Bioinformatics/ Pipeline for running bioinformatic analyses. ###
 1\_0\_Genomes/ **Buchnera_aphidicola_Mper_CP002697_annot.[gff/gtf]**
 *Buchnera* annotations for accession CP002697 in GFF file format. No
 header. | *Column number* | *Type* | *Description* | | :-------------- |
 :-------- | :------------ | | 1 | Character | Sequence | | 2 | Character |
 Source | | 3 | Character | Type | | 5 | Integer | Start | | 6 | Integer |
 End | | 7 | Character | Quality | | 8 | Character | Strand | | 9 |
 Character | Details | **Buchnera_aphidicola_Mper_CP002697_annot.tsv**
 *Buchnera* annotations for accession CP002697 in TSV file format. |
 *Column name* | *Type* | *Description* | | :------------ | :-------- |
 :---------------- | | Name | Character | Sequence | | Type | Character |
 Type | | Minimum | Integer | Start | | Maximum | Integer | End | | Length
 | Integer | Annotation length | | Direction | Character | Strand |
 **Buchnera_aphidicola_Mper_CP002697_cds.gtf** *Buchnera* coding sequence
 annotations for accession CP002697 in GFF file format. No header. |
 *Column name* | *Type* | *Description* | | :------------ | :-------- |
 :------------ | | 1 | Character | Sequence | | 2 | Character | Source | |
 3 | Character | Type | | 5 | Integer | Start | | 6 | Integer | End | | 7 |
 Character | Quality | | 8 | Character | Strand | | 9 | Character | Details
 | **Buchnera_aphidicola_Mper_CP002697_genome.[fasta/gb]** *Buchnera*
 genome sequences for accession CP002697 in FASTA and GB file format.
 **Buchnera_aphidicola_Mper_CP002697_prot.[faa]** *Buchnera* protein
 sequences for accession CP002697 in FASTA file format.
 **Myzus_persicae_CloneG006_AphidBase_v3.fasta** *Myzus persicae* genome
 for clone G006 in Aphid Base (version 3) in FASTA file format. ###
 1\_4\_Variant\_calling/ **vars_for_snpeff_buch.vcf** Variants for
 performing *SnpEff* analyses on *Buchnera* in VCF file format.
 **vars_snp_nomiss_5x_gpa.vcf** Variants for performing population genetic
 analyses on *Myzus persicae* in VCF file format.
 **vars_snp_nomiss_20x_buch.vcf** Variants for performing population
 genetic analyses on *Buchnera* in VCF file format. ###
 1\_5\_Diversity\_filter/ **sites_cov_buch_20x.csv** Number of sites
 covered in the *Buchnera* genome at a depth of 20x. | *Column name* |
 *Type* | *Description* | | :------------ | :-------- |
 :------------------------------- | | Sample | Character | Sample ID | |
 Sites\_tot | Character | Total sites | | Sites\_cov | Character | Sites
 covered at depth threshold | **sites_cov_gpa_10x.csv** Number of sites
 covered in the *Myzus persicae* genome at a depth of 10x. | *Column name*
 | *Type* | *Description* | | :------------ | :-------- |
 :------------------------------- | | Sample | Character | Sample ID | |
 Sites\_tot | Character | Total sites | | Sites\_cov | Character | Sites
 covered at depth threshold | **sites_het_buch_20x.csv** Number of sites
 that are heterozygous in the *Buchnera* genome at a depth of 20x. |
 *Column name* | *Type* | *Description* | | :------------ | :-------- |
 :----------------- | | Sample | Character | Sample ID | | Sites\_het |
 Character | Heterozygous sites | **sites_het_gpa_10x.csv** Number of sites
 that are heterozygous in the *Myzus persicae* genome at a depth of 10x. |
 *Column name* | *Type* | *Description* | | :------------ | :-------- |
 :----------------- | | Sample | Character | Sample ID | | Sites\_het |
 Character | Heterozygous sites | ### 1\_6\_SnpEff\_Buchnera
 **snpEff_annot_raw_buch.vcf** Annotations of *Buchnera* variants using
 *SnpEff* in VCF file format. **snpEff_annot_parsed.csv** Annotations of
 *Buchnera* variants using *SnpEff* parsed and in CSV file format. |
 *Column number* | *Type* | *Description* | | :-------------- | :-------- |
 :-------------------- | | CHROM | Character | Sequence name | | POS |
 Integer | Sequence position | | LOCUS | Character | Locus ID | | REF |
 Character | Reference allele | | ALT | Character | Alternate allele | |
 SEQ | Character | Focal allele sequence | | EFFECT | Character | Mutation
 effect | | GENE | Character | Gene function | | AMINO | Character | Amino
 acid | | TYPE | Character | Type of mutation | ## Data:
 2\_Popgen\_analysis/ This data directory is left empty and is used as a
 placeholder. This is the pipeline for running population genetic analyses.
 ## Scripts for Bioinformatics Any script with the name ‘1_[…]’ is part of
 the ‘1_Bioinformatics’ pipeline. The ‘[…]’ indicates details of the
 script’s role. **1_0_R_environment.R** R script to set up environmental
 variables in R. **1_2_Trim_reads._HPC_jobs.R** R script to generate
 multiple scripts for trimming samples in parallel on an HPC.
 **1_3a_Prepare_references.sh** Bash script to prepare reference sequences
 for mapping. **1_3b_Map_GPA_HPC_jobs.R** R script to generate multiple
 scripts for mapping samples to the *Myzus persicae* genome in parallel on
 an HPC. **1_3c_Map_Buch_HPC_jobs.R** R script to generate multiple scripts
 for mapping samples to the *Buchnera* genome in parallel on an HPC.
 **1_3d_Downsample_BAMs.sh** Bash script to downsample BAM files.
 **1_4a_Variant_calling_GPA.sh** Bash script to call variants in the *Myzus
 persicae* genome. **1_4b_Variant_calling_Buchnera.sh** Bash script to call
 variants in the *Buchnera* genome. **1_4c_Import_and_filter_SNPs.R** R
 script to import and filter called variants.
 **1_4d_Genotype_probabilities_GPA.R** R script to obtain genotype
 probabilities from low-coverage *Myzus persicae* variants.
 **1_5a_Diversity_filter_GPA_10x.sh** Bash script to obtain genetic
 diversity statistics for *Myzus persicae*.
 **1_5b_Diversity_filter_Buch_20x.sh** Basch script to obtain genetic
 diversity statistics for *Buchnera*. **1_6a_SnpEff_Buchnera_annotate.sh**
 Bash script to annotate mutational effects on *Buchnera* variants with
 *SnpEff*. **1_6b_SnpEff_Buchnera_parse.R** R script to parse the *SnpEff*
 annotations of *Buchnera* variants. **1_Bioinformatics.Rproj** R project
 file to manage R analyses. ## Scripts for Population genetic analysis Any
 script with the name ‘2_[…]’ is part of the ‘2_Popgen_analysis’ pipeline.
 The ‘[…]’ indicates details of the script’s role. **2_0_R_environment.R**
 R script to set up environmental variables in R.
 **2_3_Genetic_differentiation.R** R script for calculating genetic
 differentiation among samples. **2_4_Genetic_distance_trees.R** R script
 for constructing genetic distance trees. **2_5_Diversity_stats.R** R
 script for summarising diversity statistics. **2_6_Plant_hosts.R** R
 script for testing for genetic segregation among plant hosts.
 **2_7_Buchnera_mutations.R** R script for summarising the mutational
 effects in the *Buchnera* genome. **2_Popgen_analysis.Rproj** R project
 file to manage R analyses. ## Sharing/Access information Whole-genome
 sequencing data used in this study came from Singh et al (2021) and de
 novo sequenced samples generated specifically for this work. Source and
 GenBank SRA accessions are listed below. | Source | Sample | SRA Accession
 | | ----------------- | ---------- | ------------- | | Singh et al. 2021 |
 CHI-S-71 | SRR13326401 | | Singh et al. 2021 | CHI-S-72 | SRR13326400 | |
 Singh et al. 2021 | SK-S-82 | SRR13326389 | | Singh et al. 2021 | AUS-S-43
 | SRR13326432 | | Singh et al. 2021 | AUS-S-45 | SRR13326430 | | Singh et
 al. 2021 | AUS-S-46 | SRR13326429 | | Singh et al. 2021 | AUS-S-47 |
 SRR13326428 | | Singh et al. 2021 | BEL-S-103 | SRR13326480 | | Singh et
 al. 2021 | FRA-S-21 | SRR13326456 | | Singh et al. 2021 | ITA-S-37 |
 SRR13326439 | | Singh et al. 2021 | UK-S-11 | SRR13326483 | | Singh et al.
 2021 | UK-S-118 | SRR13326475 | | Singh et al. 2021 | UK-S-3 | SRR13326457
 | | Singh et al. 2021 | USA-S-G006 | SRR3466613 | | Singh et al. 2021 |
 AUS-S-42 | SRR13326433 | | Singh et al. 2021 | AUS-S-44 | SRR13326431 | |
 Singh et al. 2021 | AUS-S-48 | SRR13326427 | | Singh et al. 2021 |
 AUS-S-49 | SRR13326426 | | Singh et al. 2021 | AUS-S-50 | SRR13326425 | |
 Singh et al. 2021 | ITA-S-110 | SRR10199541 | | Singh et al. 2021 |
 ITA-S-33 | SRR13326443 | | Singh et al. 2021 | SPA-S-77 | SRR13326395 | |
 Singh et al. 2021 | UK-S-105 | SRR10199546 | | Singh et al. 2021 | UK-S-29
 | SRR13326448 | | This study | AUS-Z-1 | SRR25064225 | | This study |
 AUS-Z-10 | SRR25064204 | | This study | AUS-Z-11 | SRR25064223 | | This
 study | AUS-Z-12 | SRR25064222 | | This study | AUS-Z-13 | SRR25064221 | |
 This study | AUS-Z-14 | SRR25064220 | | This study | AUS-Z-17 |
 SRR25064218 | | This study | AUS-Z-23 | SRR25064211 | | This study |
 AUS-Z-15 | SRR25064219 | | This study | AUS-Z-18 | SRR25064217 | | This
 study | AUS-Z-19 | SRR25064216 | | This study | AUS-Z-20 | SRR25064215 | |
 This study | AUS-Z-21 | SRR25064214 | | This study | AUS-Z-3 | SRR25064213
 | | This study | AUS-Z-4 | SRR25064210 | | This study | AUS-Z-6 |
 SRR25064208 | | This study | AUS-Z-7 | SRR25064207 | | This study |
 AUS-Z-8 | SRR25064206 | | This study | AUS-Z-9 | SRR25064205 |