Zilong-Li/array-imputation-workflow

imputation workflow for array data using shapeit2 and impute2

Overview

Latest release: None, Last update: 2024-02-18

Linting: linting: failed, Formatting: formatting: failed

Topics: genotype-imputation snakemake-workflows snp-array

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/Zilong-Li/array-imputation-workflow . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

General Settings

To configure this workflow, modify config/config.yaml according to your needs, following the explanations provided in the file.

Reference Panels Sheet

There are 5 mandatory columns chr,vcf,phased,trios andgeneticmap needed to be defined in the reference panel sheet. The phased column only validates yes and no string. The trios column defines a path of plink fam file or na. The geneticmapdefines a path of genetic map used by shapeite2 and impute2.

  • refpanel1.tsv is mandatory. The vcf can be phased or unphased. This is primarily used as panel for prephasing the chip array data.
  • refpanel2.tsv is optional. The vcf can be phased or unphased. If this is defined in config.yaml, then impute2 will use both refpanel1 and refpanel2 as reference panels.

Input Files

The chip array genotype data must be in plink formats required by shapeite2. Therefore you must define bed, bim and fam in config.yaml.

Scenarios

Several scenarios are defined in config.yaml. One can choose to run each scenario individually or all.

snakemake -j60 --config scenario=phasing -n

Linting and formatting

Linting results

 1Traceback (most recent call last):
 2  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/cli.py", line 1922, in args_to_api
 3    any_lint = workflow_api.lint()
 4               ^^^^^^^^^^^^^^^^^^^
 5  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/api.py", line 337, in _handle_no_dag
 6    return method(self, *args, **kwargs)
 7           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 8  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/api.py", line 354, in lint
 9    workflow.include(
10  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/workflow.py", line 1391, in include
11    exec(compile(code, snakefile.get_path_or_uri(), "exec"), self.globals)
12  File "/tmp/tmp9ibmeosk/workflow/Snakefile", line 27, in <module>
13    include: "rules/imputation.smk"
14  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/workflow.py", line 1391, in include
15    exec(compile(code, snakefile.get_path_or_uri(), "exec"), self.globals)
16  File "/tmp/tmp9ibmeosk/workflow/rules/common.smk", line 25, in <module>
17    vcfDupPos = config["vcf_dupPos"]
18                ~~~~~~^^^^^^^^^^^^^^^
19KeyError: 'vcf_dupPos'

Formatting results

 1[DEBUG] 
 2<unknown>:1: SyntaxWarning: invalid escape sequence '\#'
 3<unknown>:1: SyntaxWarning: invalid escape sequence '\#'
 4[DEBUG] 
 5[DEBUG] In file "/tmp/tmp9ibmeosk/workflow/Snakefile":  Formatted content is different from original
 6[DEBUG] 
 7[DEBUG] In file "/tmp/tmp9ibmeosk/workflow/rules/merge.smk":  Formatted content is different from original
 8[DEBUG] 
 9[DEBUG] In file "/tmp/tmp9ibmeosk/workflow/rules/imputation.smk":  Formatted content is different from original
10[DEBUG] 
11[INFO] 3 file(s) would be changed 😬
12[INFO] 2 file(s) would be left unchanged 🎉
13
14snakefmt version: 0.10.0