Quality Control in Sequencing Data: A Day in My Grad Student Life

Working with sequencing data is a lot like opening a mysterious treasure chest; there is the thrill of discovery, but also the risk that what is inside might not be as valuable as you hoped. Hidden within those files could be the genomic equivalent of gold or just a lot of noise. This is where Quality Control (QC) becomes indispensable.

In my recent learning from NASA's Genelab On-demand course, we focused on Sequencing data QC, the crucial first step before diving into analysis. No matter how advanced your downstream methods are, poor-quality input will always produce unreliable output. In bioinformatics, the saying “garbage in, garbage out” couldn’t be more true.

Why QC Matters?

When we get raw data from sequencing machines i.e. FASTQ files, it is not perfect. Errors creep in due to base-calling mistakes, adapter contamination, overrepresented sequences, or even leftover PCR duplicates. If we skip QC, we might spend hours or days analyzing flawed data, only to get misleading results. And in science, misleading results are worse than no results at all.

In our session, we used tools like FastQC to generate detailed reports. Think of it as a health check-up for your sequencing reads. It shows per-base quality scores, GC content, sequence duplication levels, and overrepresented sequences.

Then, to actually FIX the problems, we applied Trimmomatic (or similar trimmers) to remove adapters and low-quality bases. The idea is to keep the sequences that can actually be trusted for downstream analysis like alignment, assembly, or variant calling.

Reading the QC Reports

Opening the FastQC HTML reports felt like reading a detailed diagnostic sheet for a patient; only in this case, the “patient” is my dataset.

• Per base sequence quality: The green zone is our happy place; red means trouble.

• Adapter content: If this graph spikes, trimming is non-negotiable.

• GC content: Should roughly match the organism’s genome; weird patterns can mean contamination.

My Takeaways

1. QC is not optional. It is the seatbelt of sequencing analysis.

2. Automation is great, but you still need human eyes to interpret the reports.

3. Bad quality data can sometimes be salvaged, sometimes it is better to let it go.

At the end of the day, sequencing data QC feels like the lab’s version of cleaning your room; not glamorous, but absolutely necessary before you can do the fun stuff. And once you have cleaned up, you can trust that what you are working on is worth the time you will spend analyzing it.