baerlachlan/smk-rnaseq-counts
Snakemake workflow for estimating read counts from RNA-seq data
Overview
Topics:
Latest release: v1.2.5, Last update: 2025-02-27
Linting: linting: failed, 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/baerlachlan/smk-rnaseq-counts . --tag v1.2.5
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
.
The workflow requires configuration by modification of config/config.yaml
.
Follow the explanations provided as comments in the file.
The configuration of samples and units is specified as tab-separated value (.tsv
) files.
Each .tsv
requires specific columns (see below), but extra columns may be present (however, will not be used).
The default path for the sample sheet is config/samples.tsv
.
This may be changed via configuration in config/config.yaml
.
samples.tsv
requires only one column named sample
, which contains the desired names of the samples.
Sample names must be unique, corresponding to a physical sample.
Biological and technical replicates should be specified as separate samples.
The default path for the unit sheet is config/units.tsv
.
This may be changed via configuration in config/config.yaml
.
units.tsv
requires four columns, named sample
, unit
, fq1
and fq2
.
Each row of the units sheet corresponds to a single sequencing unit.
Therefore, for each sample specified in samples.tsv
, one or more sequencing units should be present.
unit
values must be unique within each sample.
A common example of an experiment with multiple sequencing units is a sample split across several runs/lanes.
For each unit, the respective path to FASTQ
files must be specified in the fq1
and fq2
columns.
Both columns must exist, however, the fq2
column may be left empty in the case of single-end sequencing experiments.
This is how one specifies whether single- or paired-end rules are run by the workflow.
Linting and formatting
Linting results
Using workflow specific profile workflow/profiles/default for setting default command line arguments.
Lints for rule genome_get (line 1, /tmp/tmpe2s6in1d/baerlachlan-smk-rnaseq-counts-87e0dc2/workflow/rules/refs.smk):
* No log directive defined:
Without a log directive, all output will be printed to the terminal. In
distributed environments, this means that errors are harder to discover.
In local environments, output of concurrent jobs will be mixed and become
unreadable.
Also see:
https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
Lints for rule transcriptome_get (line 13, /tmp/tmpe2s6in1d/baerlachlan-smk-rnaseq-counts-87e0dc2/workflow/rules/refs.smk):
* No log directive defined:
Without a log directive, all output will be printed to the terminal. In
distributed environments, this means that errors are harder to discover.
In local environments, output of concurrent jobs will be mixed and become
unreadable.
Also see:
https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
Lints for rule annotation_get (line 25, /tmp/tmpe2s6in1d/baerlachlan-smk-rnaseq-counts-87e0dc2/workflow/rules/refs.smk):
... (truncated)
Formatting results
None