snowman828 / publication_abley2020_seedgerminationvariability

Analysis code for Abley & Formosa et al 2020

Code for Abley & Formosa et al 2020

This repository holds the data analysis code for Abley & Formosa et al 2020. For the code used in simulations of the gene regulatory network please see the accompanying GitLab repository.

All of the code (and data) is deposited as a static snapshot in the Cambridge Apollo repository (doi link to be added once published), corresponding to release (release to be made once published) of the GitHub repository.

The code in the GitHub repository is non-static and may be updated if we find any bugs (see "NEWS" section at the bottom).

Setup

1. Clone the repository (or download as a zip file and extract the files) - you should then have a project directory called "publication_Abley2020_SeedGerminationVariability".
2. Download and extract the zip files with the data from the Appolo repository. Place them inside the project directory.

You should then have this directory structure:

publication_Abley2020_SeedGerminationVariability
          ├── data
          └── scripts

Running the analysis

Most of the data analysis was done using R.

To recreate the analysis and figures for the paper, open the 2020-Abley_Germination.Rproj RStudio project file (which will ensure you have the correct working directory set - R scripts use relative paths from the project directory).

Some details about the files on scripts/R:

• All scripts named with prefix Fig recreate each figure in the paper. Some of the graphs were slightly edited in inkscape for the published version.
• The scripts in data_processing do different data processing steps and produce files already provided in the data folder in our repository. However, they can be used to recreate those files.
  • bsa_run_scans.R - run the bulk segregant mapping analysis using QTLSeqr.
  • qtl_run_scans.R - fit the QTL models on the MAGIC lines using the MagicHelpR package.
  • GerminationData_MakingLongTables.R - takes the raw phenotype data and reshapes it into a suitable format for figures.
• The scripts in functions contain some custom functions used in some of the analysis (they are loaded from other scripts).

---

Whole-genome sequence data for bulk-segregant mapping of the No x Col-0 F2 was analysed using stand-alone command line tools.

The code for this analysis is in scripts/shell. The scripts are numbered in the order they should be run. We appologise that some of the paths are hard-coded, but the analysis should be fully documented in the code.

The main output of these scripts is the file ColxNo_all_naive.tsv (SNP calls using freebayes' naive variant calling method), which we provide in the data repository.

Further details

The following data files are provided in our repository:

Data/
├── BSAmapping
│   ├── ColxNo_all_naive.tsv
│   ├── QTLseqr_input.tsv
│   └── QTLseqr_results.csv
├── ColxNo_F2germ.csv
├── ColxNo_F3germ.csv
├── cyp707a1_ga3oxMutantsGerm.csv
├── GAandABAdoseResponse.csv
├── germ_summaryForExpComparison.csv
├── germ_summaryForExpComparisonCV.csv
├── HeatShockExperimentGerm.csv
├── MAGICParents.csv
├── MAGICParentsRawData.csv
├── MAGICs.csv
├── MAGICsRawData.csv
├── QTLmapping
│   ├── germ_summaryPerLineForQTLMapping.tsv
│   ├── magic_gen_object.rds
│   ├── qtl_scan_all.csv
│   └── qtl_scan_no_outlier.csv
├── SingleSiliquesGermRawData.csv
├── Soil_vs_plates.csv
├── soil_vs_platesRawData.csv
├── SpanishAccessions.csv
├── SpanishAccessionsRawData.csv
├── statsVsSurvivalCombined234.csv
└── SummaryCVSowingTimes.csv

Figures 1 & 3

Data files MAGICsRawData.csv, MAGICParentsRawDat.csv and SpanishAccessionsRawData.csv contain germination time data for the MAGIC lines, the MAGIC parental accessions and the Spanish accessions respectively. soil_vs_platesRawData.csv contains germination time data for MAGIC lines and accessions used in the soil vs plate comparison in Fig.1 Figure Supplement 1G.

These files were read into the GerminationData_MakingLongTables.R script, which converts the daily germination counts into a long format for further analysis. This script outputs the following files: MAGICs.csv, MAGICParents.csv, SpanishAccessions.csv, Soil_vs_plates.csv.

These files were read into Fig01_03_MAGICdataAnalysis.R which was used to summarise the data and perform the analyses used to generate Fig. 1 and its supplements and Fig. 3 and its supplements. The Fig01_03_MAGICdataAnalysis.R script outputs the file germ_summaryPerLineForQTLMapping.tsv (file found in data/QTLmapping) which contains summary statistics for each MAGIC line used for QTL mapping. This script also uses the files germ_summaryForExpComparison.csv, germ_summaryForExpComparisonCV.csv and SummaryCVSowingTimes.csv for intermediate steps in the analysis used to generate Fig. 1C (see comments in script Fig01_03_MAGICdataAnalysis.R for full details).

Figure 2

SingleSiliquesGermRawData.csv contains germination time data for single and half siliques and was analysed with the script Fig02_SingleandHalfSiliques.R to generate the plots for Fig.2 and Fig.2, Figure Supplement 1.

Figure 4

Figure 4 was generated using files QTLmapping/qtl_scan_no_outlier.csv, QTLmapping/qtl_scan_all.csv and BSAmapping/QTLseqr_results.csv using Fig04-qtl_bsa_mapping.R.

Figure 4, Figure Supplement 3 was generated by using data file ColxNo_F2germ.csv with R script Fig04_Sup3_Col x No F2germ.R.

Figure 4, Figure Supplement 4 was generated by using data file ColxNo_F3germ.csv with R script Fig04_Sup4_ColxNoF3_germination.R

Figure 5 & 6

See the accompanying simulations repository

Figure 7

Figure 7 and its supplement were generated by using data file GAandABAdoseResponse.csv with script Fig07_ABAandGA_doseResponse.R

Figure 8

Figure 8 was generated by using data file cyp707a1_ga3oxMutantsGerm.csv with the script Fig08_GA_ABAmutants_germ.R

Figure 9

Figure 9 and its supplement was generated by using data file HeatShockExperimentGerm.csv with script Fig09_HSexperimentSurvival.R. The file statsVsSurvivalCombined234.csv is used as an intermediate step in this analysis (see R script for details).