RNA-Seq QC: Ensuring Data Quality

Title: Garbage In, Garbage Out: A Practical Guide to QC for RNA-Seq

In our last post, we organized our raw FASTQ files and saw how Snakemake can automate merging. Now, we must confront a critical truth of bioinformatics: no data is perfect. This is why Quality Control (QC) is an indispensable step. Skipping it can lead to flawed results and wasted time.

This guide will walk you through the key tools and, more importantly, show you exactly what to look for in their output.

Our QC Toolkit: FastQC, fastp, and MultiQC

Our strategy uses three tools in concert:

fastp: To clean the data by trimming adapters and low-quality bases.
FastQC: To run a final quality check on the cleaned data.
MultiQC: To aggregate all the reports into a single summary for easy review.

Interpreting QC Reports: What to Look For

When you open a FastQC or fastp report, you'll see many plots. For RNA-Seq, here are the most important ones to focus on.

1. Per Base Sequence Quality

What it shows: The distribution of quality scores for each position along the reads. The y-axis is the quality score (higher is better), and the x-axis is the base position in the read.
What you want to see (Good): Quality scores should be high across the entire read (in the green zone, >28). A slight, gradual drop towards the end of the read is normal.
What to watch out for (Bad): A sharp drop in quality in the second half of the reads, with the median score falling into the orange or red zones.
What it means for RNA-Seq: Low-quality bases at the ends of reads can cause them to be mapped incorrectly or not at all. This reduces the number of usable reads for your analysis.
The Fix: This is a common issue, and fastp automatically trims these low-quality ends by default.

2. Adapter Content

What it shows: The percentage of your reads that contain sequences from common sequencing adapters.
What you want to see (Good): All lines should be flat at or very near 0%. This means no adapter contamination.
What to watch out for (Bad): A line that starts at zero and rises sharply towards the end of the reads.
What it means for RNA-Seq: This happens when the RNA fragment being sequenced is shorter than the read length, so the sequencer reads past the biological sequence and into the adapter. These adapter sequences are artificial and will prevent the read from aligning to the genome.
The Fix: This is critical to remove. fastp is excellent at automatically detecting and trimming adapter sequences. After running fastp, this plot in a new FastQC report should be clean.

3. Per Sequence GC Content

What it shows: The distribution of average GC content across all reads.
What you want to see (Good): A relatively smooth, single peak that is roughly bell-shaped (a normal distribution). For human or mouse RNA-Seq, this peak is typically around 45-55% GC.
What to watch out for (Bad):
- A sharp, secondary peak at a different GC content.
- A very broad or distorted peak.
What it means for RNA-Seq: This can be a red flag for contamination. For example, a second peak might indicate that another organism's DNA/RNA is present in your sample. It can also indicate a technical artifact from library preparation.
The Fix: Unlike the previous issues, this is not something a tool can easily "fix." This plot provides crucial diagnostic information. If you see a major abnormality here, you may need to investigate the sample's source or even consider excluding it from your analysis.

4. Duplication Levels

What it shows: The percentage of reads that are exact copies of another read.
What it means for RNA-Seq: This is a tricky one. In DNA sequencing, high duplication often indicates a technical problem (PCR over-amplification). However, in RNA-Seq, a certain level of duplication is expected because you are sequencing a population of molecules with vastly different abundances. A few very highly expressed genes will naturally produce many identical reads.
What to watch out for: An extremely high duplication rate (e.g., >50-60%) combined with a low number of total reads might suggest a low-complexity library where PCR amplification has gone wrong. You can't "fix" this, but it's important context for your final analysis.

The Power of MultiQC

Reviewing these plots for one sample is informative. Reviewing them for 20 samples is tedious. MultiQC solves this by parsing all your individual QC reports and presenting the key stats in a single, comparative report. This makes it incredibly easy to spot an outlier sample that, for example, has much lower quality or higher adapter content than all the others.

Next Up

Now that we know how to ensure our reads are clean and high-quality, we are finally ready to find out where they came from. In the next post, we'll tackle Alignment, the process of mapping our reads to a reference genome.

Garbage In, Garbage Out: A Practical Guide to QC for RNA-Seq