jianhong/pairedtag

A snakemake workflow for single-cell multiomic data analysis pipeline

Overview

Topics:

Latest release: None, Last update: 2023-07-19

Linting: linting: failed, Formatting:formatting: failed

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/jianhong/pairedtag . --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 configuration

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

Sample and unit setup

The sample and unit setup is specified via tab-separated tabular files (.tsv). Missing values can be specified by empty columns or by writing NA.

sample sheet

The default sample sheet is config/samples.tsv (as configured in config/config.yaml). Each sample refers to an actual physical sample, and replicates (both biological and technical) may be specified as separate samples. For each sample, you will always have to specify a sample_name.

unit sheet

The default unit sheet is config/units.tsv (as configured in config/config.yaml). For each sample, add one or more sequencing units (for example if you have several runs or lanes per sample).

.fastq file source

For each unit, you will have to define a source for your .fastq files. This can be done via the columns fq1, fq2, with two .fastq files for paired-end reads.

adapter trimming

TODO

Linting and formatting

Linting results

Workflow defines that rule get_genome is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule get_annotation is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule genome_faidx is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule bwa_index is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule cellranger_index is eligible for caching between workflows (use the --cache argument to enable this).
Lints for snakefile /tmp/tmp8w84n72k/workflow/rules/common.smk:
    * Path composition with '+' in line 9:
      This becomes quickly unreadable. Usually, it is better to endure some
      redundancy against having a more readable workflow. Hence, just repeat
      common prefixes. If path composition is unavoidable, use pathlib or
      (python >= 3.6) string formatting with f"...".
      Also see:


Lints for snakefile /tmp/tmp8w84n72k/workflow/rules/ref.smk:
    * Path composition with '+' in line 3:
      This becomes quickly unreadable. Usually, it is better to endure some
      redundancy against having a more readable workflow. Hence, just repeat
      common prefixes. If path composition is unavoidable, use pathlib or
      (python >= 3.6) string formatting with f"...".

... (truncated)

Formatting results

[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/remap_barcode.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/cellranger.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/common.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/qc.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/ref.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/Snakefile":  Formatted content is different from original
[INFO] 6 file(s) would be changed 😬

snakefmt version: 0.8.4