snakemake-workflows/rna-seq-star-deseq2

RNA-seq workflow using STAR and DESeq2

Overview

Latest release: v3.0.1, Last update: 2025-09-05

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Topics: snakemake sciworkflows reproducibility gene-expression-analysis deseq2

Wrappers: bio/bwa/index bio/fastp bio/multiqc bio/reference/ensembl-annotation bio/reference/ensembl-sequence bio/samtools/faidx bio/sra-tools/fasterq-dump bio/star/align bio/star/index

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/snakemake-workflows/rna-seq-star-deseq2 . --tag v3.0.1

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 configuration

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

DESeq2 differential expression analysis configuration

To successfully run the differential expression analysis, you will need to tell DESeq2 which sample annotations to use (annotations are columns in the samples.tsv file described below). This is done in the config.yaml file with the entries under diffexp:. The comments for the entries should give all the necessary infos and linkouts. But if in doubt, please also consult the DESeq2 manual.

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. In addition, all variables_of_interest and batch_effects specified in the config/config.yaml under the diffexp: entry, will have to have corresponding columns in the config/samples.tsv. Finally, the sample sheet can contain any number of additional columns. So if in doubt about whether you might at some point need some metadata you already have at hand, just put it into the sample sheet already—your future self will thank you.

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 and sra, with either of:

  1. A single .fastq file for single-end reads (fq1 column only; fq2 and sra columns present, but empty). The entry can be any path on your system, but we suggest something like a raw/ data directory within your analysis directory.

  2. Two .fastq files for paired-end reads (columns fq1 and fq2; column sra present, but empty). As for the fq1 column, the fq2 column can also point to anywhere on your system.

  3. A sequence read archive (SRA) accession number (sra column only; fq1 and fq2 columns present, but empty). The workflow will automatically download the corresponding .fastq data (currently assumed to be paired-end). The accession numbers usually start with SRR or ERR and you can find accession numbers for studies of interest with the SRA Run Selector. If both local files and an SRA accession are specified for the same unit, the local files will be used.

strandedness of library preparation protocol

To get the correct geneCounts from STAR output, you can provide information on the strandedness of the library preparation protocol used for a unit. STAR can produce counts for unstranded (none - this is the default), forward oriented (yes) and reverse oriented (reverse) protocols.
Enter the respective value into a strandedness column in the units.tsv file.

adapter trimming and read filtering

Finally, you can provide settings for the adapter trimming with fastp (see the fastp documentation) via the units.tsv columns fastp_adapters and fastp_extra. However, if you leave those two columns empty (no whitespace!), fastp will auto-detect adapters and the workflow will set sensible defaults for trimming of RNA-seq data. If you use this automatic inference, make sure to double-check the Detected read[12] adapter: entries in the resulting fastp HTML report. This is part of the final snakemake report of the workflow, or can be found in the sample-specific folders under results/trimmed/, once a sample has been processed this far. If the auto-detection didn’t work at all (empty Detected read[12] adapter: entries), or the Occurrences in the Adapters section are lower than you would expect, please ensure that you find out which adapters were used and configure the adapter trimming manually:

In the column fastp_adapters, you can specify known adapter sequences to be trimmed off by fastp, including the command-line argument for the trimming. For example, specify the following string in this column: --adapter_sequence=AGATCGGAAGAGCACACGTCTGAACTCCAGTCA --adapter_sequence_r2=AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT If you don’t know the adapters used, leave this empty (an empty string, containing no whitespace), and fastp will auto-detect the adapters that need to be trimmed. If you want to make the auto-detection explicit for paired-end samples, you can also specify --detect_adapter_for_pe.

In the column fastp_extra, you can specify further fastp command-line settings. If you leave this empty (an empty string, containing no whitespace), the workflow will set its default:

--trim_poly_x --poly_x_min_len 7 --trim_poly_g --poly_g_min_len 7

Lexogen 3’ QuantSeq adapter trimming

For this data, adapter trimming should automatically work as expected with the use of fastp. The above-listed defaults are equivalent to an adaptation of the Lexogen read preprocessing recommendations for 3’ FWD QuantSeq data with cutadapt. The only difference is that we don’t do any length filtering with these defaults. If you want to exactly mirror the Lexogen recommendations, please use this for the fastp_extra column in your units.tsv:

--length_required 20 --trim_poly_x --poly_x_min_len 7 --trim_poly_g --poly_g_min_len 7

The fastp equivalents, including minimal deviations from the recommendations, are motivated as follows:

  • -m: In cutadapt, this is the short version of --minimum-length. The fastp equivalent is --length_required.

  • -O: Here, fastp doesn’t have an equivalent option, so we currently have to live with the suboptimal default of 4. This is greater than the min_overlap=3 used here; but smaller than the value of 7, a threshold that we have found avoids removing randomly matching sequences when combined with the typical Illumina max_error_rate=0.005.

  • -a "polyA=A{20}": This can be replaced by fastp’s dedicated option for --trim_poly_x tail removal (which is run after adapter trimming).

  • -a "QUALITY=G{20}": This can be replaced by fastp’s dedicated option for the removal of artifactual trailing Gs in Illumina data from machines with a one channel or two channel color chemistry: --trim_poly_g. This is automatically activated for earlier Illumina machine models with this chemistry, but we recommend to activate it manually in the fastp_extra column of your config/units.tsv file for now, as there are newer models that are not auto-detected, yet. Also, we recommend to set --poly_g_min_len 7, to avoid trimming spurious matches of G-only stretches at the end of reads.

  • -n: With the dedicated fastp options getting applied in the right order, this option is not needed any more.

  • -a "r1adapter=A{18}AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=3;max_error_rate=0.100000": We remove A{18}, as this is handled by --trim_poly_x. fastp uses the slightly higher min_overlap equivalent of 4, which is currently hard-coded (and not exposed as a command-line argument). Because of this, we cannot set the max_error_rate to the Illumina error rate of about 0.005.

  • -g "r1adapter=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=20": This is not needed any more, as fastp searches the read sequence for adapter sequences from the start of the read (see the fastp adapter search code).

  • --discard-trimmed: We omit this, as adapter sequence removal early in the read will leave short remaining read sequences that are subsequently filtered by --length_required.

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