STEP 6: Visualization

Visualization is essential for exploring RNA-seq data quality and expression patterns. This page covers creating genome browser tracks and expression plots.

Creating BigWig Files

BigWig files allow visualization of RNA-seq coverage in genome browsers.

Using bamCoverage (deepTools)

On the CCB:

module load deeptools

Create normalized coverage tracks:

bamCoverage -b sample.bam -o sample.bw \
  --binSize 10 \
  --normalizeUsing CPM \
  --numberOfProcessors 8

Strand-specific BigWig files:

For stranded RNA-seq, create separate tracks for forward and reverse strands:

# Forward strand
bamCoverage -b sample.bam -o sample_forward.bw \
  --filterRNAstrand forward \
  --normalizeUsing RPKM \
  --numberOfProcessors 8

# Reverse strand
bamCoverage -b sample.bam -o sample_reverse.bw \
  --filterRNAstrand reverse \
  --normalizeUsing RPKM \
  --numberOfProcessors 8

bamCoverage Parameters

• -b - Input BAM file
• -o - Output bigWig file
• --binSize - Size of bins in bp (default 50)
• --normalizeUsing RPKM - Reads Per Kilobase per Million normalization
• --filterRNAstrand - For stranded libraries (forward/reverse)
• --numberOfProcessors - Number of threads

Viewing in Genome Browsers

UCSC Genome Browser

Make copies of your bigwigs to the /project/higgslab/datashare/your_username and then use the public link to upload bigWig files as custom tracks or use track hubs to visualize RNA-seq coverage alongside gene annotations.

Next Steps

After visualization:

1. Validate expression patterns make biological sense
2. Check for batch effects or outliers
3. Proceed to gene counts and differential expression analysis (STEP 7)
4. Generate publication-quality figures