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: 1. Raw data processing → Run nf-core/rnaseq on HiperGator using SLURM configuration 2. Quality control & differential analysis → Upload count matrices to DECODeR for comprehensive QC and differential expression analysis 3. 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.