SiYangming/riboseq.smk

Snakemake workflow for Ribo-seq (RPFs + Totals + Downstream)

Overview

Latest release: v1.0.0, Last update: 2026-10-03

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=SiYangming/riboseq.smk

Quality control: linting: passed formatting: passed

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. 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/SiYangming/riboseq.smk . --tag v1.0.0

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 4: Run workflow

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

To run the workflow using apptainer/singularity, use

snakemake --cores all --sdm apptainer

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.

Workflow overview

Ribo-seq (RPFs) plus matched total RNA-seq (Totals), then Downstream R analysis. Logic matches SiYangming/Ribo-seq (run.sh pipelines RPFs / Totals / Downstream).

Input data

Sample sheet (config/samples.tsv):

sample	fastq_1	fastq_2	type	treatment
rpf1	path/to/rpf.fastq.gz		riboseq	control
tot1	path/to/R1.fastq.gz	path/to/R2.fastq.gz	rnaseq	treated

type: riboseq (RPF, typically SE) or rnaseq (Totals, SE or PE). Empty fastq_2 means single-end.

Set FASTA / GTF / region-length paths in config/config.yaml. entrypoint: all | totals | rpfs | downstream | qc.

exec_mode: native | conda | container (Apptainer; set each tool container_image).

Minimal FastQC smoke: bash run_smk.sh --directory .test --cores 2 with .test/config (entrypoint: qc). Full chr20 data: bash .test/fetch_testdata.sh.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Required
exec_mode	string	yes
sample_sheet	string	yes
entrypoint	string	yes
threads	integer
skip_umi	boolean
use_star	boolean
rrna_fasta	string
trna_fasta	string
pc_fasta	string	yes