plycrsk/rna-seq-star-deseq2

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Overview

Topics:

Latest release: v1.2.1, Last update: 2022-01-18

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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/plycrsk/rna-seq-star-deseq2 . --tag v1.2.1

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

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.

Sample and unit sheet

  • Add samples to config/samples.tsv. For each sample, the columns sample_name, and condition have to be defined. The condition (healthy/tumor, before Treatment / after Treatment) will be used as contrast for the DEG analysis in DESeq2. To include other relevant variables such as batches, add a new column to the sheet.
  • For each sample, add one or more sequencing units (runs, lanes or replicates) to the unit sheet config/units.tsv. By activating or deactivating mergeReads in the config/config.yaml, you can decide wether to merge replicates or run them individually. For each unit, define adapters, and either one (column fq1) or two (columns fq1, fq2) FASTQ files (these can point to anywhere in your system). Alternatively, you can define an SRA (sequence read archive) accession (starting with e.g. ERR or SRR) by using a column sra. In the latter case, the pipeline will automatically download the corresponding paired end reads from SRA. If both local files and SRA accession are available, the local files will be preferred. To choose the correct geneCounts produced by STAR, you can define the strandedness of a unit. STAR produces counts for unstranded ('None' - default), forward oriented ('yes') and reverse oriented ('reverse') protocols.

Missing values can be specified by empty columns or by writing NA.

DESeq scenario

To initialize the DEG analysis, you need to define a model in the config/config.yaml. The model can include all variables introduced as columns in config/samples.tsv.

  • The standard model is ~condition - to include a batch variable, write ~batch + condition.

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