IMS-Bio2Core-Facility/polya_liftover
A Snakemake Workflow for using PolyA_DB and UCSC LiftOver with CellRanger
Overview
Topics: bioinformatics-pipeline snakemake cellranger liftover workflow single-cell-rna-seq reproducibility python shell transcriptomics fastqc multiqc
Latest release: v2.0.1, Last update: 2022-02-20
Linting: linting: passed, Formatting:formatting: passed
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.
When using Mamba, run
mamba create -c conda-forge -c bioconda --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
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/IMS-Bio2Core-Facility/polya_liftover . --tag v2.0.1
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
To run the workflow using apptainer
/singularity
, use
snakemake --cores all --sdm apptainer
To run the workflow using a combination of conda
and apptainer
/singularity
for software deployment, use
snakemake --cores all --sdm conda apptainer
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
.
Configuration
The configuration keys that are expected are given below. Don't worry about typos, etc. These are all enforced with Snakemake's brilliant schema validation.
samples.yaml
The top level keys are the lanes from the sequencer. The second level keys are the samples from that lane. The third level keys are the paths to the R1 and R2 data.
lift.yaml
Top level keys are gene names. Each gene must have 3 values:
- chr - the chromosome
- start - the feature start coordinate
- end - the feature end coordinate
Coordinates should be exactly as given by PolyA_DB
config.yaml
samplesheet
Path to the samplesheet.
This defaults to config/samples.yaml
.
lift
Path to the "lift sheet" - the PolyA-DB outputs.
This defaults to config/lift.yaml
.
get_cellranger
- url: str, required. Url to retrive the CellRanger binary.
get_gtf
- url: str, required. Url to retrieve the reference GTF.
get_fa
- url: str, required. Url to retrieve the reference primary assembly.
get_9_to_10
- url: str, required. Url to retrieve the
mm9
tomm10
over.chain forLiftOver
get_10_to_39
- url: str, required. Url to retrieve the
mm10
tomm39
over.chain forLiftOver
cellranger
- introns: bool, required. Whether or not to include introns in the allignment. Essentially specifies if the data is single-nucleus or single cell.
- n_cells: int, required. The number of cells to expect in the sample.
- mem: int, required. The local memory, in Gb, available to CellRanger.
Linting and formatting
Linting results
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Formatting results
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