Bo_demography

Demographic analysis of Brassica oleracea

Alignment of sequence data

Follows GATK4 best practices (https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145)

Implemented as a Snakemake workflow (https://snakemake.readthedocs.io/en/stable/index.html)

TODO

• Clean up filtering steps
• Add adjustable parameters & metrics for filtering criteria (depth, missingness)
• More efficient use of scratch space
• Include sample ids & read groups in bwa-mem step (thanks, Michelle!): bwa mem -R $(echo "@RG\tID:${LANE}_${SAMPLE}\tSM:$SAMPLE\tLB:${SAMPLE}.1\tPL:ILLUMINA") -a $B73FA $R1 $R2
• Add a config file to specify samples/reference/paths/etc. (will make workflow more general)
  • include variable for scratch directory
• add rule for generating mappability mask using SNPable
• Find a better solution to create input for ADMIXTURE (right now requires manual edit of chr names and snp ids)

Workflow

Snakefile provides general organization (names of samples, calls other scripts/rules)

/rules contains rules (*.smk) that tell snakemake what jobs to run; organized broadly into categories

1. mapping.smk - map reads to reference genome

   • download fastq files (fasterq-dump)
   • trim adapters (Trimmomatic PE)
   • map to reference genome (bwa mem; B. oleracea HDEM reference)
   • sort BAM files (gatk/SortSam)
   • mark duplicates with Picard (gatk/MarkDuplicates)
   • add read groups with Picard (gatk/AddOrReplaceReadGroups)

2. calling.smk - call variants

   • identify raw SNPs/indels (gatk/HaplotypeCaller)
   • combine GVCFs (gatk/GenomicsDBImport)
   • joing genotyping (gatk/GenotypeGVCFs)

3. filtering.smk - filter SNPs

   • select SNPs (gatk/SelectVariants)
   • apply hard filters (gatk/VariantFiltration)
   • export diagnostics (qual, quality depth, depth, marker quality, etc.) (gatk/VariantsToTable)
   • remove sites with missing data and select biallelic SNPs (gat/SelectVariants)
   • filter on individual sample depth (gatk/VariantFiltration + gatk/SelectVariants)
   • export filtered depth for each accession (gatk/VariantsToTable)
   • combine vcfs into a single file (bcftools concat)

4. smc.smk - estimate population size history with SMC++

   • convert vcf to smc format - requires mappability mask (smc++ vcf2smc)
   • fit population size history with cross-validation (smc++ cv)
   • fit population size history without cross-validation (smc++ estimate)

5. admixture.smk - maximum likelihood estimation of individual ancestries

   • create input file (some bash commands + LD pruning in plink)
   • run admixture (admixture)

To run the workflow:

1. Create a conda environment (only need to do this once)
   conda env create bo-demography --file environment.yaml

   • to access for future use, start a screen session with screen and use:
     source activate bo-demography

2. If running on a cluster, update the cluster config file (submit.json)

3. Dry run to test that everything is in place... snakemake -n

4. Submit the workflow with ./submit.sh

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── data           <- Scripts to download or generate data
│   ├── features       <- Scripts to turn raw data into features for modeling
│   ├── models         <- Scripts to train models and then use trained models to make
│   │                     predictions
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations

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Project based on the cookiecutter data science project template. #cookiecutterdatascience