🧬 De novo RNA-seq Assembly and Differential Expression Analysis

This repository documents a de novo RNA-seq transcriptome analysis pipeline using Trinity, RSEM, edgeR, TransDecoder, and BLAST+.
It is designed for tumor-free and tumor-bearing Drosophila melanogaster larvae samples.

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📂 Dataset Information

• Organism: Drosophila melanogaster
• Conditions:
  • Control (tumor-free larvae)
  • Treated (tumor-bearing larvae)
• Input Data: Adapter-trimmed paired-end FASTQ files

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🔧 Tools & Dependencies

Make sure the following tools are installed before running the pipeline:

• Trinity
• RSEM
• Bowtie
• edgeR (R package)
• TransDecoder
• BLAST+
• samtools
• assembly-stats

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🚀 Workflow Overview

Steps

1. Concatenate FASTQ samples
2. Convert FASTQ → FASTA
3. Perform de novo assembly with Trinity
4. Calculate assembly statistics (assembly-stats)
5. Estimate transcript abundance with RSEM
6. Generate count matrix (abundance_estimates_to_matrix.pl)
7. Perform differential expression with edgeR
8. Predict coding regions with TransDecoder
9. Functional annotation with BLASTp (against UniProt)

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📊 Outputs

• Assembly: Trinity.fasta, Assembly_Statistics.txt
• Abundance Estimates: RSEM output directories
• Count Matrix: abundance_count.isoform.counts.matrix
• Differential Expression: edgeR results (DE_results.txt)
• Predicted ORFs: TransDecoder .pep and .cds files
• Functional Annotation: BLAST results (result.txt)

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📖 References

• Trinity RNA-seq
• RSEM
• edgeR Bioconductor
• TransDecoder
• BLAST+

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✍️ Author

Devraj Pokhrel
B.Sc. Biotechnology