tucca-cellag/tucca-rna-seq is a modular RNA-Seq workflow developed in the
Kaplan Lab at TUCCA. It provides a complete, end-to-end pipeline that
processes raw FASTQ files through quality control, quantification, differential
expression, and pathway enrichment analysis.
Initially tailored for cellular agriculture research on muscle and fat cell transcriptomes, the workflow has been designed with modularity and flexibility at its core. This allows users to easily customize the pipeline by adding, removing, or modifying steps to suit a broad range of RNA-Seq applications and experimental designs.
This workflow was developed using the Snakemake workflow management
system. tucca-rna-seq is a standardized usage Snakemake workflow that follows
the best practices laid out in the Snakemake documentation (as of
Snakemake v9.3) and can be found in the Snakemake Workflow Catalog.
Looking Ahead: AI-Powered Analysis Integration
Beyond v1.0.0, we're developing an innovative AI-powered analysis platform that will integrate directly with this workflow. This platform will feature a private Large Language Model (LLM) chat application connected to your RNA-Seq results, enabling natural language queries about your data. The system will automatically vectorize workflow outputs and provide intelligent, context-aware analysis assistance.
The usage of this workflow is described in our documentation at tucca-cellag.github.io. If you've found a bug or there is a feature that we're missing (in the workflow or in our documentation) please open an issue to let us know.
Before running the workflow, we recommend reviewing our Data Collection Template to ensure you have all necessary information organized. This template helps you:
- Organize your raw sequencing data
- Document sample metadata and experimental design
- Specify analysis parameters and preferences
- Plan for quality control and visualization needs
For detailed workflow documentation, visit tucca-cellag.github.io.
Created in https://BioRender.com
Created via `snakemake --rulegraph`
For questions or suggestions regarding the workflow, first, check out our detailed documentation at tucca-cellag.github.io. If you can't find the answer to your question in our documentation you can try checking if someone has previously opened an issue answering your question. If you still have a question please open an issue so we can help! For any other inquiries, please contact us via email.
If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and its DOI (above if available). You should also cite the individual tools used in this workflow. See CITATIONS.md for details.
Cellular Agriculture is a cutting-edge field that harnesses biotechnology to produce agricultural products directly from cells. Unlike traditional farming, which relies on raising and harvesting whole organisms, cellular agriculture focuses on cultivating animal cells in controlled environments to create sustainable alternatives for meat, dairy, and other animal-derived products.
- Sustainability 🌍: Reduces the environmental impact associated with conventional agriculture, including lower greenhouse gas emissions, reduced land and water usage, and minimized waste production.
- Ethical Considerations 🐮❤️: Offers humane alternatives by eliminating the need for animal slaughter, addressing animal welfare concerns.
- Food Security 🍽️: Enhances the ability to produce food in areas with limited agricultural resources, contributing to global food security.
- Innovation and Research 🔬: Drives advancements in biotechnology, genetics, and bioinformatics, fostering interdisciplinary collaboration and novel scientific discoveries.
We welcome your involvement in the development of this workflow via submission
of bug reports, proposing new features, engaging in discussions, or providing
fixes and other code modifications. If you're interested in contributing,
please consult the contributing guidelines. For all interactions within
the tucca-cellag community, we ask that you observe our code of conduct.