Snakemake workflow: rna-seq-xengsort

A Snakemake workflow for separating host and graft sequencing reads from RNAseq data using xengsort:

• Usage
• Deployment options
• Workflow profiles
• Authors
• References

xengsort was chosen, as it clearly outperformed all other tools in a recent independent benchmarking study that also thoroughly evaluated the effects of mouse host genome contamination. For full citations, see the references below

Usage

The usage of this workflow is described in the Snakemake Workflow Catalog. This includes a visualization of the workflow diagram and a table with all workflow parameters.

Detailed information about input data and workflow configuration can also be found in the config/README.md.

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository or its DOI.

Deployment options

To run the workflow from command line, change the working directory.

cd path/to/snakemake-workflow-name

Adjust options in the default config file config/config.yaml. Before running the complete workflow, you can perform a dry run using:

snakemake --dry-run

To run the workflow with test files using conda:

snakemake --cores 2 --sdm conda --directory .test

Workflow profiles

The profiles/ directory can contain any number of workflow-specific profiles that users can choose from. The profiles README.md provides more details.

Authors

• David Lähnemann
  • German Cancer Consortium (DKTK), partner site Essen-Düsseldorf, A partnership between DKFZ and University Hospital Essen
  • https://orcid.org/0000-0002-9138-4112
• Pankaj Singroul
  • German Cancer Consortium (DKTK), partner site Essen-Düsseldorf, A partnership between DKFZ and University Hospital Essen
  • Bridge Institute of Experimental Tumor Therapy (BIT), Division of Solid Tumor Translational Oncology (DKTK), West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
  • Institute of Cell Biology (Cancer Research), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
  • https://orcid.org/0000-0001-9855-1828

References

Zentgraf, J., Rahmann, S. Fast lightweight accurate xenograft sorting. Algorithms Mol Biol 16, 2 (2021). https://doi.org/10.1186/s13015-021-00181-w

Bhandari, M., He, F., Rogojina, A. et al. Benchmarking mouse contamination removing protocols in patient-derived xenografts genomic profiling. npj Precis. Onc. 9, 113 (2025). https://doi.org/10.1038/s41698-025-00902-z

Chen, Shifu. 2025. “ fastp 1.0: An Ultra-Fast All-Round Tool for FASTQ Data Quality Control and Preprocessing.” iMeta 4, e70078. https://doi.org/10.1002/imt2.70078

Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. Sustainable data analysis with Snakemake. F1000Research, 10:33, 10, 33, 2021. https://doi.org/10.12688/f1000research.29032.2.