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RNA-seq to Differential Expression - Usage Guide

Overview

This workflow takes you from raw RNA-seq FASTQ files to a list of differentially expressed genes. It combines quality control, quantification, and statistical analysis into a complete pipeline.

Prerequisites

# CLI tools
conda install -c bioconda fastp salmon star subread

# R packages
install.packages('BiocManager')
BiocManager::install(c('DESeq2', 'tximport', 'apeglm'))
install.packages(c('ggplot2', 'pheatmap', 'ggrepel'))

Quick Start

Tell your AI agent what you want to do:

  • "Run the RNA-seq to DE workflow on my FASTQ files"
  • "I have paired-end RNA-seq data, help me find differentially expressed genes"
  • "Process my RNA-seq from raw reads through DESeq2"

Example Prompts

Starting from FASTQ

"I have FASTQ files for 3 control and 3 treated samples, run the full RNA-seq pipeline"

"Quantify my RNA-seq with Salmon and run DESeq2"

"Use STAR alignment instead of Salmon for my RNA-seq"

Customizing the workflow

"Run the pipeline but use a custom GTF file"

"Add batch correction to my RNA-seq analysis"

"Skip the QC step, my reads are already trimmed"

From quantification to DE

"I already have Salmon quant files, just run DESeq2"

"Import my featureCounts output into DESeq2"

Input Requirements

Input Format Description
FASTQ files .fastq.gz Paired-end reads (R1 and R2 per sample)
Sample metadata CSV/TSV Sample names and experimental conditions
Reference FASTA + GTF Transcriptome for Salmon, genome + GTF for STAR
tx2gene CSV Transcript-to-gene mapping (for Salmon)

What the Workflow Does

  1. Quality Control - Trim adapters and low-quality bases with fastp
  2. Quantification - Count reads per gene/transcript (Salmon or STAR+featureCounts)
  3. Import - Load counts into R with proper normalization offsets
  4. Differential Expression - Run DESeq2 statistical analysis
  5. Results - Export significant genes and visualizations

Choosing Between Paths

Criterion Salmon Path STAR Path
Speed Faster (no alignment) Slower
Storage Lower (no BAM files) Higher (BAM files)
Use case Standard DE analysis Need BAMs for other analyses
Accuracy Excellent for DE Excellent for DE
Novel junctions No Yes (with 2-pass)

Tips

  • Replicates: Minimum 3 biological replicates per condition (more is better)
  • Sequencing depth: 20-30M reads per sample is typical for DE analysis
  • Library type: Salmon auto-detects, but verify with --libType A
  • Batch effects: If samples were processed in batches, include batch in the design formula
  • Outliers: Check PCA plot; remove severe outliers or investigate sample swaps