Is your team treating QC as a checkbox—or as the foundation of reliable biomarker discovery?

Your RNA-seq biomarker study just failed. The culprit? You skipped quality control.

I've seen many promising biomarker programs crash because teams rushed straight to differential expression analysis without properly QC'ing their data.

Here's what proper RNA-seq QC catches before it derails your program:

🧬 rRNA contamination - High rRNA content signals degraded samples that will produce unreliable biomarker signatures
📊 Alignment quality issues - Poor mapping rates indicate sequencing problems that compromise gene quantification accuracy
📈 Coverage bias - 3'/5' bias and uneven transcript coverage can create false biomarker signals
🔄 Duplication artifacts - High PCR duplication rates artificially inflate expression levels of certain transcripts
⚖️ GC bias - Systematic bias toward certain sequences that skews expression measurements
🎯 Batch effects - Technical variation between sequencing runs can create false biomarker signals that disappear when you validate

Most teams discover these issues after they've already identified "promising" biomarkers.

By then, you've wasted months and burned through precious sample inventory.

The FDA-backed SEQC consortium showed that rigorous QC isn't optional—it's what separates reproducible biomarker discoveries from expensive false starts.

Bottom line: QC your RNA-seq data like your program depends on it. Because it does.