Frustration of manual interpretation
Recently, a client reached out to us with a familiar frustration. Their team was spending hours going through MultiQC reports across large sequencing batches, scanning the same sections again and again to extract the same set of metrics. The bioinformatician was not struggling with interpretation. They knew exactly what they were looking for. The problem was repetition. Every report required manually locating per base quality trends, duplication rates, adapter content, GC bias and coverage summaries before deciding whether a sample could move forward.
One evening, that frustration escalated. Their sequencing core delivered 48 exome runs at once. The workflow was routine. FASTQ files went through FastQC. MultiQC aggregated the results into a consolidated report. But consolidation did not equal clarity. The bioinformatics lead still had to page through dense plots and tables for each batch, validating thresholds, checking context and mentally filtering noise from signals. Community discussions often mention how exhausting this can become, even for experienced analysts. That evening proved the point.
What the client needed was not another QC tool. They needed a way to extract exactly the information they cared about, consistently, without manually digging through every plot. If a system could summarise the report and surface only what mattered, the job would become faster and more focused. Bioinformaticians could spend less time confirming pass or fail and more time on actual science, troubleshooting edge cases and improving pipelines.
That request became the starting point for building an AI assisted QC reporting layer on top of existing MultiQC tools.
AI is assistance, not replacement
When introducing AI into clinical or research pipelines, the first concern is whether machines are meant to replace human expertise. The answer here is no. The system we built prepares an executive summary and ranked list of QC findings so you can focus on what matters. It serves exactly the information a bioinformatician looks for and provides actionable insights based on that information, but it never edits your data or makes the decision for you. You remain in control, the AI acts as a knowledgeable assistant, not a replacement.
Technical architecture of the pipeline
We developed the AI‑assisted QC pipeline using Nextflow. We chose Nextflow because it provides scalable, reproducible and portable workflows, simplifies combining different tools and encapsulates dependencies in containers so the same pipeline runs anywhere.
Key modules and flow:
- Extracts the header line to capture instruments and run information.
- Runs quality checks on each sample.
- Aggregates QC outputs into one HTML report.
- Gathers relevant QC metrics and constructs a prompt for the language model.
- Uses the prompt to generate an executive summary with ranked issues and suggestions.
- Inserts the AI-generated summary at the top of the MultiQC report.
In short, Nextflow gave us a portable and reproducible workflow and the modular design lets us swap components or run the pipeline anywhere with minimal effort.
Future scope
This proof‑of‑concept demonstrates that AI can meaningfully assist QC reporting, but several extensions could enhance its utility:
- Track trends: Collect AI summaries across runs to spot instrument drift or batch effects.
- Model fine‑tuning: Train the AI on past QC decisions to adapt it to your data and reduce mistakes.
- Structured outputs: Return JSON with fields like module, severity, evidence, and suggested command for easier automation.
- Confidence scores: Attach a confidence level to each recommendation so you know when to double‑check.
- LIMS integration: Feed summaries into your lab management system to update sample records and log decisions.
- Clinical compliance: Validate the pipeline, log all prompts and responses, and document changes to meet regulatory requirements.
An intelligent layer of triage
Manual quality control of next generation sequencing data becomes demanding as sample volumes increase. Even with consolidated reports, experts still need to interpret dozens of metrics, validate thresholds and confirm whether each sample is suitable for downstream analysis. That repetition introduces fatigue and increases the risk of oversight.
The AI assisted pipeline described here builds on existing QC workflows and adds an intelligent layer of triage. It highlights what matters, ranks issues and offers actionable guidance while keeping final authority with the bioinformatician. Built on Nextflow for scalability, reproducibility and modularity, the system can be integrated seamlessly into existing environments. For laboratories facing similar QC bottlenecks, this approach offers a plug-and-play solution to their current workflow rather than a disruptive replacement.
