Skip to content

Latest commit

 

History

History
83 lines (57 loc) · 5.37 KB

README.md

File metadata and controls

83 lines (57 loc) · 5.37 KB
Raw

GeneHackman

DOI

A pipeline for performing common genetic epidemiology tasks at the University of Bristol.

Goals:

  • Implement comment steps in GWAS investigations to create reproducible, more efficient research
  • Reusable pipelines that can be utilised on different projects
  • Shared code and steps that can be updated according to the latest knowledge and practices

Available Pipelines

Here is a list of available pipelines, and the steps they run

standardise_gwas.smk compare_gwases.smk disease_progression.smk qtl_mr.smk
Takes in any of: vcf, csv, tsv, txt (and zip, gz) All steps in 'standardise_gwas.smk' All steps in 'standardise_gwas.smk' All steps in 'standardise_gwas.smk'
Convert Reference Build
(GRCh38 -> GRCh37)		 PLINK clumping Runs some collider bias corrections, compares results Run MR against top hits of specific QTL dataset
Populate RSID from CHR, BP, EA, and OA Calculate heterogeneity between GWASes Miami Plot of Collider Bias Results Volcano Plot of Results
Converts z-scores and OR to BETA LDSC h2 and rg Expected vs. Observed Comparison Run coloc of significant top hit MR results
Auto-populate GENE ID <-> ENSEMBL ID Expected vs. Observed Comparison
Unique SNP = CHR:BP_EA_OA
(EA < OA alphabetically)

Onboarding

1. Clone the repository into your personal space on BlueCrystal 4

git clone git@github.com:MRCIEU/GeneHackman.git && cd GeneHackman

2. Ensure you have conda installed and initialised before activating

conda activate /mnt/storage/private/mrcieu/data/genomic_data/pipeline/genehackman

(you can alternatively create your own conda environment if you like: conda env create --file environment.yml)

3. Populate .env and input.json files

cp .env_example .env

  • populate the DATA_DIR, RESULTS_DIR and RDFS_DIR environment variables in .env file These should probably be in your work or scratch space (/user/work/userid/...)
  • RDFS_DIR is optional. All generated files can be copied automatically. Please ensure the path ends in working/

Fill out input.json file

4. Run the pipeline

./run_pipeline.sh snakemake/<specific_pipeline>.smk <optional_input_file.json>

  • run_pipeline.sh is just a convience wrapper around the snakemake command, if you want to do anything out of the ordinary, please read up on snakemake
  • If there are errors while running the pipeline, you can find error messages either directly on the screen, or in slurm log file that is outputted on error
  • It is recommended that you run the your pipeline inside a tmux session.

How it works:

The standard column naming for GWASes are:

CHR BP EA OA BETA SE P EAF SNP RSID

A full list of names and default values can be found here

There are 3 main components to the pipeline

  1. Snakemake to define the steps to complete for each pipeline.
  2. Docker / Singularity container with installed languages (R and python), packages, os libraries, and code
  3. Slurm: each snakemake step spins up a singularity container inside a slurm job. Each step can specify different slurm requirements.

Repository Organisation

  • R directory holds R package code that can also be called and reused by any step in the pipeline (accessed by a cli script)
  • scripts directory holds the scripts that can be easily called by snakemake (Rscript example.R --input_ex example_input)
  • snakemake directory, which defines the pipeline steps and configuration, and shared code between pipelines
  • docker directory holds the information for creating the docker image that the pipeline runs
  • tests directory holds all R tests, and a end to end pipeline test script

Making changes

If you want to make any additions / changes please contact andrew.elmore@bristol.ac.uk, or open an issue in this repo.