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tucca-rna-seq: An RNA-Seq Workflow

The tucca-rna-seq workflow is an end-to-end workflow for RNA-Seq data analysis. It provides a fast, automated pipeline for researchers and bioinformaticians, regardless of their programming experience.

Originally developed for cellular agriculture research, this workflow is designed to be a reliable, user-friendly toolset that streamlines RNA-Seq data analysis, ensuring accuracy, reproducibility, and scalability for any research field.

Our goal is to accelerate research by providing a powerful, accessible, and well-documented workflow. For those interested in the cellular agriculture origins of this project, you can learn more in the CAAIL primer.

Workflow Overview

Section titled “Workflow Overview”
A diagram of the tucca-rna-seq workflow

High-level overview of the tucca-rna-seq workflow. Created in https://BioRender.com

A Case Study in Cellular Agriculture

Section titled “A Case Study in Cellular Agriculture”

An early development version of tucca-rna-seq was central to a published study on cellular adaptation for cultivated meat. It helped identify the biological mechanisms that govern how cells transition from adherent to suspension growth.

The case study shows how the workflow can be used to translate raw sequencing data into deeper biological understanding.

About the Workflow

Section titled “About the Workflow”

The tucca-rna-seq workflow provides an efficient pipeline for RNA-Seq data analysis. Key features include:

Data Processing

Section titled “Data Processing”
  • Quality Control: Implements FastQC and Qualimap for quality assessment with configurable parameters.
  • Quantification: Employs Salmon for fast, accurate transcript quantification, accounting for experimental attributes and biases.
  • Meta-Analysis: Aggregates results using MultiQC for a unified overview of data quality and processing metrics.

Advanced Differential Expression Analysis

Section titled “Advanced Differential Expression Analysis”
  • Statistical Tools: Uses DESeq2 for differential expression analysis.
  • Flexible Configurations: Supports multiple experimental designs and contrasts in a single run, with configurable statistical parameters.
  • Data Transformation: Provides variance-stabilizing transformations (rlog, vst) for downstream analysis.

Functional Enrichment

Section titled “Functional Enrichment”

The workflow integrates multiple enrichment analysis approaches:

  • Standard Analysis: GO, KEGG, and WikiPathways using clusterProfiler.
  • MSigDB Integration: Access to 8 major gene set collections with custom GMT file support.
  • SPIA Analysis: Topology-based pathway impact analysis.
  • Harmonizome Database: Tissue-specific gene sets from various expression datasets.
  • Custom Gene Sets: Support for user-defined gene sets and pathways.

Post-Analysis Interactive Tools

Section titled “Post-Analysis Interactive Tools”
  • RMarkdown Playgrounds: Interactive notebooks for data exploration and visualization.
  • PCA Explorer: Interactive principal component analysis and sample clustering.
  • GeneTonic Integration: Enrichment-analysis visualization with exportable figures.
  • Custom Analysis: A framework for extending analysis beyond workflow outputs.

High Reproducibility

Section titled “High Reproducibility”
  • Environment Management: Employs conda and renv to replicate computational environments, ensuring consistency.
  • Container Integration: Uses Singularity/Apptainer containers for consistent execution environments.
  • Schema Validation: JSON schema validation for configuration files.
  • Version Control: Maintains version-controlled workflows with Snakemake and git for tracking and replication.

Scalability and Flexibility

Section titled “Scalability and Flexibility”
  • HPC Integration: Optimized for high-performance computing clusters.
  • Multiple Execution Environments: Support for Slurm, LSF, Kubernetes, and cloud platforms.
  • Modular Design: Easily extensible and customizable.
  • Testing Infrastructure: CI/CD testing with GitHub Actions.

Modern Snakemake Integration

Section titled “Modern Snakemake Integration”
  • Snakemake v8+: Built for the latest Snakemake with modern executor plugins.
  • Executor Plugins: Dedicated plugins for different computing environments.
  • Simplified Profiles: Easy-to-configure execution profiles.
  • Resource Management: Intelligent resource allocation and job scheduling.

Workflow Rule Graph

Section titled “Workflow Rule Graph”

The diagram below illustrates the rule graph (DAG) for the tucca-rna-seq Snakemake workflow. Each node represents a workflow rule, and edges indicate dependencies between steps. This provides a high-level view of how data moves through the pipeline from raw input to final results.

A diagram of the tucca-rna-seq workflow rule graph (DAG)

Rule graph (DAG) of the tucca-rna-seq Snakemake workflow.

Getting Started

Section titled “Getting Started”
  • Data Collection: Why and how to collect the metadata you’ll need for your analysis.
  • Snakemake Primer: An introduction to the core concepts behind Snakemake and reproducible workflows.
  • Deployment Strategies: An overview of the different platforms where you can run this workflow.
  • Installation: How to install the required software for the workflow.
  • Configuration: How to configure the workflow for your specific analysis.
  • Running the Workflow: How to execute the workflow and monitor its progress.

Platform-Specific Guides

Section titled “Platform-Specific Guides”

Additional Resources

Section titled “Additional Resources”

Connect With Us

Section titled “Connect With Us”

If you have questions, feedback, or need assistance, reach out through our channels:

Alternatively, visit our GitHub Repository to explore more of TUCCA’s projects.

A project hub of the Tufts University Center for Cellular Agriculture · Content licensed MIT · GitHub organization · Get help

Linked external resources are independent of TUCCA and Tufts University and remain under their own licenses.