🔬 Bulk RNA-Seq Series – Post 10: Understanding GTF & GFF Files for Feature Annotation Link to heading

1️⃣ Introduction: From Aligned Reads to Biological Insight Link to heading

Once your reads are aligned (typically producing BAM or SAM files), the next major step in your RNA-Seq pipeline is annotation. Alignment tells you where your reads landed on the genome — but annotation tells you what they hit:

• A gene?

• An exon?

• A retroelement?

Without reliable annotations, aligned reads are just genomic coordinates with no biological meaning. That’s where GTF and GFF files come in.

2️⃣ What Are GTF and GFF Files? Link to heading

GTF (Gene Transfer Format) and GFF (General Feature Format) are plain-text files that define the locations and types of features on a genome. These include:

• 📍 Exons

• 📍 Introns

• 📍 Coding sequences (CDS)

• 📍 UTRs

• 📍 Transcripts

• 📍 Genes

Each line in a GTF or GFF file describes one feature and includes:

• Chromosome name

• Feature type (e.g., exon, CDS)

• Start and end coordinates

• Strand

• Gene ID, transcript ID, and biotype (in the attributes column)

These annotations guide tools like featureCounts and HTSeq-count on how to assign reads to features.

3️⃣ GTF vs. GFF – A Quick Comparison Link to heading

✅ Bottom Line: Link to heading

Both formats define the biological meaning behind your alignments. Choose based on tool compatibility and annotation availability.

4️⃣ Why Annotation Matters Link to heading

Annotations shape how we interpret read alignments:

• 🔍 Where do your reads land?

• 🔄 What gene or transcript do they belong to?

• ⚠️ What biotype or biological role is associated?

📌 Changing your annotation file can completely change your results — even with the same BAM file.

Whether you use GENCODE, Ensembl, RefSeq, or a custom annotation, your choice impacts:

• Gene-level counts

• Transcript isoform analysis

• Novel element detection

5️⃣ Real-Life Example: ERVs (Endogenous Retroviruses) Link to heading

ERVs are repetitive elements often excluded from default annotations.

To capture ERV activity:

• 🧬 Use an ERV-specific GTF file (e.g., from RepeatMasker or curated ERV databases)

• 🔁 Re-analyze previously aligned reads with the updated annotation

This is especially valuable in:

• 🦠 Virology

• 🧫 Cancer biology

• 🧬 Immunology

Even legacy datasets can reveal new biological insights with updated annotation files.

6️⃣ Best Practices: Choosing the Right Annotation Link to heading

✔️ Source matters: GENCODE, Ensembl, and RefSeq annotations can vary widely in gene definitions and metadata
✔️ Version compatibility: Make sure your annotation matches the genome build used in alignment (e.g., GRCh38 vs GRCh37)
✔️ Custom annotations: Don’t hesitate to tailor your GTF if studying special features like ERVs, lncRNAs, or pseudogenes
✔️ Consistency is key: Use the same annotation across alignment, counting, and downstream analysis to avoid mismatches

7️⃣ What’s Next? Feature Counting Link to heading

Once your reads are aligned and annotations are ready, the next step is to count reads overlapping annotated features using tools like:

• 📏 featureCounts

• 🐍 HTSeq-count

These tools depend heavily on your GTF or GFF file to decide how to group and quantify expression.

📌 Key Takeaways Link to heading

✔️ GTF and GFF files add biological meaning to raw alignments
✔️ Annotations impact every step of the RNA-Seq pipeline
✔️ Custom annotations (e.g., ERVs) can unlock new insights from existing data
✔️ Always validate your annotation format, version, and compatibility with analysis tools

📌 Coming up: Count Matrix Generation with HTSeq & featureCounts! Stay tuned! 🚀

👇 Have you re-annotated datasets with updated or custom GTFs? Share your findings below!

#RNASeq #GTF #GFF #Transcriptomics #Bioinformatics #Genomics #ERV #Reproducibility #Annotation #NGS #DataScience #ComputationalBiology