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 columnssample_name
, andcondition
have to be defined. Thecondition
(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 deactivatingmergeReads
in theconfig/config.yaml
, you can decide wether to merge replicates or run them individually. For each unit, define adapters, and either one (columnfq1
) or two (columnsfq1
,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 columnsra
. 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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