🔬 Bulk RNA-Seq Series – Post 2: Understanding RNA-Seq Reads & FASTQ Files Link to heading

🛠 What Are RNA-Seq Reads? Link to heading

RNA sequencing (RNA-Seq) generates millions of short reads that represent fragments of transcribed RNA molecules. These reads provide valuable insights into gene expression, alternative splicing, transcript abundance, and novel transcript discovery.

✔️ Each read is a short fragment of cDNA generated during sequencing
✔️ Reads can be single-end (SE) or paired-end (PE)
✔️ Sequencing technologies generate raw reads in the FASTQ format, which we will explore in detail

Understanding RNA-Seq reads and their storage format is crucial for preprocessing and downstream analysis.

📚 The FASTQ Format – Storing Sequencing Reads Link to heading

The FASTQ file is the standard format for storing raw sequencing reads. It contains both the nucleotide sequences and quality scores for each read.

Each read in a FASTQ file consists of four lines:

1️⃣ Read Identifier – The unique read name, usually from the sequencer
2️⃣ Sequence – The actual nucleotide sequence (A, T, G, C)
3️⃣ Separator (+) – A separator line, sometimes repeating the identifier
4️⃣ Quality Scores – ASCII-encoded Phred scores indicating the confidence in each base call

Example FASTQ Entry: Link to heading

@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAAGGGTGCCCGATAG
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF

🔹 Identifier (@SEQ_ID) – Read ID generated by the sequencer
🔹 Sequence (GATTTGGGGTT...) – The actual RNA-derived sequence
🔹 Separator (+) – Marks the beginning of the quality score line
🔹 Quality Scores (!''*((...) – ASCII-encoded Phred scores indicating confidence per base

These quality scores help determine which reads are reliable and should be retained for analysis.

📊 Single-End vs. Paired-End Reads – What’s the Difference? Link to heading

RNA-Seq reads can be generated in two different formats: Single-End (SE) or Paired-End (PE).

🔹 Single-End (SE) Reads: Link to heading

✔️ Only one read per fragment is sequenced
✔️ Faster and cheaper sequencing
✔️ Suitable for gene expression quantification
✔️ Less accurate for transcript assembly or alternative splicing studies

🔹 Paired-End (PE) Reads: Link to heading

✔️ Two reads per fragment (forward & reverse strand)
✔️ Improved alignment accuracy due to extra information
✔️ Essential for alternative splicing detection and transcript assembly
✔️ More expensive & computationally intensive

💡 Choosing Between SE & PE Reads: If your goal is simple gene expression analysis, single-end reads might be sufficient. However, for isoform discovery and structural variation detection, paired-end sequencing is highly recommended.

📈 Quality Control – Ensuring High-Quality Reads Link to heading

Before aligning reads to a reference genome, it’s crucial to check sequencing quality. Poor-quality reads introduce errors, misalignments, and biases in downstream analyses.

🛠 Quality Control Tools Link to heading

✔️ FastQC – Generates per-sample quality control reports

✔️ MultiQC – Aggregates QC reports from multiple samples into a single summary

✔️ Trimmomatic & Cutadapt – Removes adapter sequences and low-quality bases

🔹 What Does FastQC Check? Link to heading

✔️ Per-base sequence quality – Detects low-quality regions in reads
✔️ Adapter contamination – Identifies non-biological adapter sequences
✔️ Overrepresented sequences – Flags possible technical artifacts
✔️ GC content distribution – Checks for biases in nucleotide composition

🔹 Running FastQC on a FASTQ File: Link to heading

fastqc sample_1.fastq.gz -o qc_reports/

✅ This generates an HTML report with detailed QC metrics.

🔄 Preprocessing FASTQ Files – Cleaning Your Data Link to heading

After quality assessment, the next step is trimming and filtering the reads. This removes low-quality bases, adapters, and contaminants, ensuring only high-quality reads proceed to alignment.

🔹 Trimming Adapters with Cutadapt: Link to heading

cutadapt -a AGATCGGAAGAGC -o sample_trimmed.fastq.gz sample_1.fastq.gz

✅ This command removes Illumina adapters and outputs a cleaned FASTQ file.

🔹 Trimming Low-Quality Bases with Trimmomatic: Link to heading

trimmomatic SE -phred33 sample_1.fastq.gz sample_clean.fastq.gz LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

✅ This removes low-quality bases while ensuring that only reads longer than 36 bases are retained.

📌 Key Takeaways Link to heading

✔️ RNA-Seq reads are stored in FASTQ files, containing both sequences and quality scores.
✔️ Paired-end reads provide better alignment accuracy and are essential for transcript assembly.
✔️ Quality control (FastQC, MultiQC) ensures that poor-quality reads do not interfere with downstream analysis.
✔️ Preprocessing (Cutadapt, Trimmomatic) removes adapter sequences and improves read quality before alignment.

📌 Next up: Quality Control with FastQC & MultiQC! Stay tuned! 🚀

👇 How do you handle FASTQ quality control in your workflow? Let’s discuss!

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