snakemake-workflows/rna-seq-kallisto-sleuth

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

Overview

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

Latest release: v2.11.1, Last update: 2025-05-02

Linting: linting: passed, Formatting: formatting: failed

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-kallisto-sleuth . --tag v2.11.1

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 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

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. For each unit, provide either of the following:

  • The path to two pairead-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 BAM file in the column bam_paired

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

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 or right here in this config/README.md section. If something is unclear, don’t hesitate to file an issue in the rna-seq-kallisto-sleuth GitHub repository.

differential expression model setup

The core functionality of this workflow is provided by the software sleuth. You can use it to test for differential expression of genes or transcripts between two or more subgroups of samples.

main sleuth model

The main idea of sleuth’s internal model, is to test a full: model (containing (a) variable(s) of interest AND batch effects) against a reduced: model (containing ONLY the batch effects). So these are the most important entries to set up under any model that you specify via diffexp: models:. If you don’t know any batch effects, the reduced: model will have to be ~1. Otherwise it will be the tilde followed by an addition of the names of any columns that contain batch effects, for example: reduced: ~batch_effect_1 + batch_effect_2. The full model than additionally includes variables of interest, so fore example: full: ~variable_of_interest + batch_effect_1 + batch_effect_2.

sleuth effect sizes

Effect size estimates are calculated as so-called beta-values by sleuth. For binary comparisons (your variable of interest has two factor levels), they resemble a log2 fold change. To know which variable of interest to use for the effect size calculation, you need to provide its column name as the primary_variable:. And for sleuth to know what level of that variable of interest to use as the base level, specify the respective entry as the base_level:.

preprocessing params

For adapter trimming, cutadapt is used, with some defaults for standard Illumina data given in the config.yaml. For more details see the comments in the config.yaml or the cutadapt documentation. For parameter suggestions for Lexogen 3’ QuantSeq data, see the section below.

For transcript quantification, kallisto is used. For details regarding its command line arguments, see the kallisto documentation.

Lexogen 3’ QuantSeq data analysis

For Lexogen 3’ QuantSeq data analysis, please set experiment: 3-prime-rna-seq: activate: true in the config/config.yaml file. For more information information on Lexogen QuantSeq 3’ sequencing, see: https://www.lexogen.com/quantseq-3mrna-sequencing/ In addition, for Lexogen 3’ FWD QuantSeq data, we recommend setting the params: cutadapt-se: with:

    adapters: "-a 'r1adapter=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=7;max_error_rate=0.005'"
    extra: "--minimum-length 33 --nextseq-trim=20 --poly-a"

This is an adaptation of the Lexogen read preprocessing recommendations for 3’ FWD QuantSeq data. Changes to the recommendations are motivated as follows:

  • -m: We switched to the easier to read --minimum-length and apply this minimum length globally. In addition, we increase the minimum length to a default of 33 that makes more sense for kallisto quantification.

  • -O: Instead of this option, minimum overlap is specified per expression.

  • -a "polyA=A{20}": We replace this with cutudapts dedicated option for --poly-a tail removal (which is run after adapter trimming).

  • -a "QUALITY=G{20}": We replace this with cutudapts dedicated option for the removal artifactual trailing Gs in NextSeq and NovaSeq data: --nextseq-trim=20.

  • -n: With the dedicated cutadapt 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 --poly-a. We increase min_overlap to 7 and set the max_error_rate to the Illumina error rate of about 0.005, both to avoid spurious adapter matches being removed.

  • -g "r1adapter=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=20": This is not needed any more, as -a option will lead to complete removal of read sequence if adapter is found at the start of the read, see: https://cutadapt.readthedocs.io/en/stable/guide.html#rightmost

  • --discard-trimmed: We omit this, as the -a with the adapter sequence will lead to complete read sequence removal if adapter is found at start, and the --minimum-length will then discard such empty reads.

meta comparisons

Meta comparisons allow for comparing two full models against each other. The axes represent the log2-fold changes (beta-scores) for the two models, with each point representing a gene. Points on the diagonal indicate no difference between the comparisons, while deviations from the diagonal suggest differences in gene expression between the treatments. For more details see the comments in the config.yaml.

Linting and formatting

Linting results

All tests passed!

Formatting results

 1[DEBUG] 
 2[DEBUG] 
 3[DEBUG] 
 4[DEBUG] 
 5[DEBUG] In file "/tmp/tmp8y3ddecn/snakemake-workflows-rna-seq-kallisto-sleuth-5130d77/workflow/rules/meta_comparisons.smk":  Formatted content is different from original
 6[DEBUG] 
 7[DEBUG] 
 8[DEBUG] 
 9[DEBUG] 
10[DEBUG] 
11[DEBUG] 
12[DEBUG] 
13[DEBUG] 
14[DEBUG] 
15[DEBUG] 
16[INFO] 1 file(s) would be changed 😬
17[INFO] 13 file(s) would be left unchanged 🎉
18
19snakefmt version: 0.10.2