RNA-seq
Recommended Analysis Workflow
Primary Recommendation: nf-core/rnaseq + DECODeR
For bulk RNA-seq projects, we recommend using nf-core/rnaseq for initial processing followed by our DECODeR application for differential expression analysis and visualization.
Workflow Overview:
- Raw data processing → Run nf-core/rnaseq on HiperGator using SLURM configuration
- Quality control & differential analysis → Upload count matrices to DECODeR for comprehensive QC and differential expression analysis
- Visualization & interpretation → Explore results through DECODeR’s interactive plots and downloadable reports
Alternative R-based Analysis
For users preferring traditional R workflows, we recommend:
- limma-voom for differential expression
- ComplexHeatmap for visualization
- Access via RStudio Server on HiperGator
Best Practices & Considerations
Experimental Design:
- Plan for adequate biological replicates (minimum 3 per condition)
- Consider batch effects and confounding variables
- Include appropriate controls for your experimental questions
Data Quality:
- Evaluate read quality and mapping rates from nf-core/rnaseq output
- Check for outlier samples using DECODeR’s QC metrics
- Assess count distribution and normalization effectiveness
Statistical Analysis:
- Set appropriate significance thresholds (FDR < 0.05, |log2FC| > 1)
- Consider multiple testing correction methods
- Validate key findings with qPCR when possible
Computational Resources:
- Use HiperGator’s SLURM scheduler for nf-core/rnaseq processing
- Scale resources based on sample count and read depth
- Plan storage needs for large fastq files and intermediate outputs
Getting Started
Submit a support request to discuss your RNA-seq project design and analysis needs.