snakemake-workflows/rna-seq-kallisto-sleuth

A Snakemake workflow for differential expression analysis of RNA-seq data with Kallisto and Sleuth.

Overview

Latest release: v4.0.0, Last update: 2025-12-18

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Topics: snakemake kallisto sleuth rna-seq differential-expression sciworkflows reproducibility

Wrappers: bio/bwa/index bio/bwa/mem bio/fastp bio/kallisto/index bio/kallisto/quant bio/reference/ensembl-annotation bio/reference/ensembl-sequence bio/samtools/index utils/datavzrd

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda. 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/snakemake-workflows/rna-seq-kallisto-sleuth . --tag v4.0.0

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 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 the following files to reflect your dataset and differential expression analysis model:

  • config/samples.tsv: samples sheet with covariates and conditions

  • config/units.tsv: (sequencing) units sheet with raw data paths

  • config/config.yaml: general workflow configuration and differential expression model setup

For the samples.tsv and units.tsv, we explain the expected columns right here, in this README.md file. For the config.yaml file, all entries are explained in detail in its comments.

samples sheet

For each biological sample, add a line to the sample sheet in config/samples.tsv. The column sample is required and gives the sample name. Additional columns can specify covariates (including batch effects) and conditions. Please ensure that these column names can be interpreted as proper variable names. Especially, they should not start with numeric chars (1vs2) or special (non-letter) symbols (+compound_x). These columns can then be used in the diffexp: models: specification section in config/config.yaml (see below).

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

units sheet

For each sample, add one or more sequencing unit lines (runs, lanes or replicates) to the unit sheet in config/units.tsv. Missing values can be specified by empty columns or by writing NA.

input file options

For each unit, provide exactly one out of the following options for input files:

  • The path to two paired-end FASTQ files in the columns fq1, fq2.

  • The path to a single-end FASTQ file in the column fq1. For single-end data, you also need to specify fragment_len_mean and fragment_len_sd, which should usually be available from your sequencing provider.

  • The path to a single-end BAM file in the column bam_single

  • The path to a paired-end BAM file in the column bam_paired

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:

--length_required 33 --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 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. In addition, we increase the minimum length to a default of 33 that makes more sense for kallisto quantification.

  • -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 with 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.

config.yaml

This file contains the general workflow configuration and the setup for the differential expression analysis performed by sleuth. Configurable options should be explained in the comments above the respective entry, so the easiest way to set it up for your workflow is to carefully read through the config/config.yaml file and adjust it to your needs. If something is unclear, don’t hesitate to file an issue in the rna-seq-kallisto-sleuth GitHub repository.

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