Bulk RNA-Seq Pipeline
The foundational method for measuring gene expression across entire populations of cells. Learn the gold-standard pipeline from fastq files to differential expression and functional enrichment.
1. Introduction to Bulk RNA-seq
Unlike single-cell RNA-seq, bulk RNA sequencing measures the average expression level of each gene across a large population of cells. This approach provides deep sequencing coverage, making it highly robust for identifying differentially expressed genes between conditions (e.g., tumor vs. normal, treated vs. untreated).
2. Quality Control & Trimming
Before analyzing the data, we must ensure the sequencing reads are of high quality. We use tools like FastQC to assess sequence quality, GC content, and adapter contamination.
Key Tools:
- FastQC: Generates quality reports for raw fastq files.
- MultiQC: Aggregates reports across many samples into a single dashboard.
- Trimmomatic / fastp: Removes adapter sequences and low-quality read ends.
3. Alignment & Quantification
Reads must be mapped to a reference genome or transcriptome. Modern pipelines often use pseudoaligners for speed, or splice-aware aligners when novel transcripts are of interest.
- STAR / HISAT2: Splice-aware aligners that generate BAM files. Require tools like `featureCounts` to generate a count matrix.
- Salmon / Kallisto: Pseudo-aligners that quantify transcript abundance directly without generating large alignment files, offering extreme speed and accuracy.
4. Differential Expression (DESeq2)
The core of bulk RNA-seq is Differential Gene Expression (DGE) analysis. DESeq2 (in R) is the industry standard for modeling count data using a negative binomial distribution.
Key steps include filtering low-count genes, estimating size factors (normalization), estimating dispersions, and applying the Wald test to find genes with significant log2 fold changes.
5. Pathway & Enrichment Analysis
A list of differentially expressed genes is just the start. We map these genes to biological pathways to understand the broader functional changes in the system.
- Gene Ontology (GO) Enrichment: Identifying over-represented biological processes.
- Gene Set Enrichment Analysis (GSEA): Analyzing the entire ranked list of genes to detect small but coordinated changes in pathways (e.g., KEGG, Reactome).
6. Publication-Ready Visualization
Communicating your results effectively requires high-quality visualizations. Key plots generated in a standard bulk RNA-seq pipeline include:
PCA Plots
Visualize sample-level variance to check for batch effects and group clustering.
Volcano Plots
Highlight the most significant and highly differentially expressed genes.
Heatmaps
Display expression patterns of top genes across all samples.
7. Master These Skills
Want to run this pipeline yourself? Our mentorship program provides raw data from real publications and guides you step-by-step through the R and command-line code.