🔬 Bulk RNA-Seq Series – Post 3: Quality Control with FastQC & MultiQC Link to heading

🛠 Why Quality Control Matters in RNA-Seq Link to heading

Before analyzing RNA-Seq data, we need to ensure that our raw reads are high quality. Poor-quality reads can introduce errors and biases, affecting alignment and differential expression analysis.

✔️ Identifies sequencing errors and adapter contamination
✔️ Detects overrepresented sequences and GC content biases
✔️ Ensures high-quality data for downstream analysis

The two main tools used for RNA-Seq quality control are FastQC and MultiQC.

📚 FastQC: Assessing Read Quality Link to heading

FastQC is the go-to tool for checking raw sequencing reads. It generates a comprehensive report on:

✔️ Per base sequence quality – Are the reads high-quality throughout?
✔️ GC content distribution – Does the dataset match expected GC levels?
✔️ Adapter contamination – Are sequencing adapters present?
✔️ Overrepresented sequences – Do specific sequences dominate the data?

➡️ Running FastQC: Link to heading

fastqc sample1.fastq.gz sample2.fastq.gz -o qc_reports/

✅ Generates an HTML report with detailed metrics on read quality.

📊 MultiQC: Aggregating Reports for Multiple Samples Link to heading

MultiQC simplifies batch processing by combining multiple FastQC reports into a single interactive report.

✔️ Summarizes QC metrics across all samples
✔️ Identifies systematic issues across datasets
✔️ Provides an easy-to-interpret visual summary

➡️ Running MultiQC: Link to heading

multiqc qc_reports/ -o multiqc_report/

✅ Produces a merged report for all samples, making it easier to identify consistent quality issues.

📈 Interpreting FastQC & MultiQC Results Link to heading

After running FastQC and MultiQC, review the reports for:

✔️ Poor quality bases (especially at read ends) – May need trimming.
✔️ Adapter sequences – Indicate contamination requiring removal.
✔️ Overrepresented sequences – Can reveal rRNA contamination or sequencing biases.
✔️ GC content deviations – Unexpected GC distribution may indicate contamination or sequencing artifacts.

🔄 Next Steps: Trimming & Filtering Low-Quality Reads Link to heading

If FastQC highlights issues like adapter contamination or low-quality bases, the next step is trimming the reads to remove unwanted sequences. This ensures only high-quality reads proceed to alignment.

🔹 What’s Next? Read Trimming with Trimmomatic & Cutadapt Link to heading

✔️ Trimmomatic – Removes low-quality bases and adapters
✔️ Cutadapt – Efficient adapter trimming for Illumina reads
✔️ FASTP – Fast and fully automated quality control

We’ll cover these tools in the next post!

📌 Key Takeaways Link to heading

✔️ FastQC assesses sequencing read quality and identifies issues.
✔️ MultiQC aggregates reports, simplifying quality control analysis for multiple samples.
✔️ Poor-quality reads impact downstream analysis, making quality control essential.
✔️ Next step: Read trimming and filtering to remove adapters and low-quality sequences.

📌 Next up: Read Trimming & Filtering with Trimmomatic! Stay tuned! 🚀

👇 How do you handle RNA-Seq quality control? Let’s discuss!

#RNASeq #Bioinformatics #FastQC #Genomics #ComputationalBiology #Transcriptomics #DataScience #OpenScience