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Citing the tucca-rna-seq Workflow

When using this workflow in your research, please cite both the workflow itself and the individual tools and methods used in the analysis.

Citing the Workflow

Section titled “Citing the Workflow”

Primary Citation

Section titled “Primary Citation”

Workflow Repository:

tucca-cellag/tucca-rna-seq: A modular RNA-Seq workflow for cellular agriculture research
https://github.com/tucca-cellag/tucca-rna-seq

DOI (Zenodo):

10.5281/zenodo.15605826

Citation Format

Section titled “Citation Format”

BibTeX:

@software{tucca_rna_seq_2025,
  title={tucca-rna-seq: A modular RNA-Seq workflow for cellular agriculture research},
  author={Bromberg, Benjamin and Kaplan, David},
  year={2025},
  url={https://github.com/tucca-cellag/tucca-rna-seq},
  doi={10.5281/zenodo.15605826}
}

APA:

Bromberg, B., & Kaplan, D. (2025). tucca-rna-seq: A modular RNA-Seq workflow
for cellular agriculture research [Computer software].
https://github.com/tucca-cellag/tucca-rna-seq

Citing Individual Tools

Section titled “Citing Individual Tools”

Core Workflow Management

Section titled “Core Workflow Management”
  • Snakemake: Köster, J., & Rahmann, S. (2012). Snakemake—a scalable bioinformatics workflow engine. Bioinformatics, 28(19), 2520-2522.

Quality Control and Alignment

Section titled “Quality Control and Alignment”
  • FastQC: Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data.
  • STAR: Dobin, A., et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21.
  • Qualimap: Okonechnikov, K., et al. (2016). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2), 292-294.
  • Salmon: Patro, R., et al. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14(4), 417-419.

Differential Expression Analysis

Section titled “Differential Expression Analysis”
  • DESeq2: Love, M. I., et al. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
  • tximeta: Love, M. I., et al. (2019). Tximeta: reference sequence checksums for provenance identification in RNA-seq analyses. F1000Research, 8.

Functional Enrichment Analysis

Section titled “Functional Enrichment Analysis”
  • clusterProfiler: Yu, G., et al. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 16(5), 284-287.
  • SPIA: Tarca, A. L., et al. (2009). A novel signaling pathway impact analysis. Bioinformatics, 25(1), 75-82.
  • MSigDB: Liberzon, A., et al. (2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12), 1739-1740.

Visualization and Reporting

Section titled “Visualization and Reporting”
  • MultiQC: Ewels, P., et al. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047-3048.
  • GeneTonic: Marini, F., & Binder, H. (2019). GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data. BMC Bioinformatics, 20(1), 1-8.
  • pcaExplorer: Marini, F., et al. (2019). pcaExplorer: an R/Bioconductor package for interactive exploration of RNA-seq principal components. Bioinformatics, 35(19), 3832-3834.
  • ideal: Marini, F., et al. (2018). ideal: an R/Bioconductor package for interactive differential expression analysis. BMC Bioinformatics, 19(1), 1-8.

Citing the Research

Section titled “Citing the Research”

Publications Using tucca-rna-seq

Section titled “Publications Using tucca-rna-seq”

If you publish research using this workflow, please:

  1. Cite the workflow using the information above
  2. Include the workflow version used in your methods
  3. Reference the GitHub repository for reproducibility
  4. Share your configuration files when possible

Example Methods Section

Section titled “Example Methods Section”
RNA-seq data analysis was performed using the tucca-rna-seq workflow
(Bromberg & Kaplan, 2025; https://github.com/tucca-cellag/tucca-rna-seq).
Raw reads were quality-controlled using FastQC and aligned to the reference
genome using STAR. Transcript quantification was performed with Salmon, and
differential expression analysis was conducted using DESeq2. Functional
enrichment analysis was performed using clusterProfiler and SPIA.

Acknowledgments

Section titled “Acknowledgments”

Funding and Support

Section titled “Funding and Support”

This workflow was developed with support from:

  • Tufts University Center for Cellular Agriculture (TUCCA)
  • Kaplan Lab at Tufts University
  • Open Source Community contributions and feedback

Contributors

Section titled “Contributors”

We thank all contributors to the workflow and documentation:

View Contributors

License and Terms

Section titled “License and Terms”

The workflow is released under the MIT License, which allows for:

  • Commercial use
  • Modification
  • Distribution
  • Private use

Requirement: Attribution must be given to the original authors.

View Full License

Contact for Citation Questions

Section titled “Contact for Citation Questions”

If you have questions about citing the workflow or need assistance with attribution:

Contact the Team

Proper citation helps support continued development and ensures scientific reproducibility. Thank you for your attention to this important aspect of scientific communication.

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