RNA-Seq Transcriptomics Pipeline

Overview

This repository contains a complete RNA-Seq analysis pipeline designed to study differential gene expression in enterobacteria under two experimental conditions: control vs treatment.

The workflow processes raw sequencing data through quality control, preprocessing, transcriptome assembly, annotation, and differential expression analysis.

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Experimental Design

• Organism: Enterobacteria
• Conditions: Control vs Treatment
• Replicates: 3 biological replicates per condition
• Sequencing: Paired-end reads (300 bp)

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Pipeline Workflow

The analysis consists of the following steps:

1. Quality Control — FastQC

• Assessment of raw read quality

• Metrics evaluated:

  • Per-base sequence quality
  • GC content
  • Sequence duplication
  • Adapter contamination

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2. Preprocessing — PRINSEQ

• Removal of low-quality reads:

  • Mean quality < 25
  • Length < 100 bp

• Trimming of ambiguous bases (Ns)

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3. De Novo Transcriptome Assembly — SPAdes

• Assembly using multiple k-mers:

  • 21, 33, 55, 77, 99, 127

• Combined reads from all samples

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4. Annotation — BLAST

• Alignment against Enterobacteria gene database

• Filtering criteria:

  • ≥ 90% identity

• Functional assignment of transcripts

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5. Mapping — Bowtie2 + SAMtools

• Alignment of reads to assembled transcriptome

• Conversion and processing:

  • SAM → BAM → sorted BAM → indexed BAM

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6. Quantification — Corset

• Clustering of transcripts
• Generation of gene-level count matrix

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7. Differential Expression — edgeR

• Statistical analysis in R

• Outputs:

  • log2 Fold Change (logFC)
  • p-values and FDR

• Significance threshold: p-value < 0.05

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Results Summary

• ~98% mapping rate across samples
• 3385 assembled transcripts
• 6675 expression clusters
• ~1200 significantly differentially expressed genes
• Clear separation between conditions in MDS analysis

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Repository Structure

.
├── README.md
├── docs/
│   ├── project_report.pdf
│   └── diagram_pipeline.jpeg
├── scripts/
│   ├── 01_fastqc.sh
│   ├── 02_prinseq.sh
│   ├── 03_spades.sh
│   ├── 04_annotation.sh
│   ├── 05_mapping.sh
│   ├── 06_quantification.sh
│   └── 07_differential_expression.R
└── data/
    └── README.md

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Full Report

A detailed explanation of methods, commands, and results is available in:

docs/project_report.pdf

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Tools Used

• FastQC
• PRINSEQ
• SPAdes
• BLAST
• Bowtie2
• SAMtools
• Corset
• edgeR (R / Bioconductor)

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References

See full bibliography in docs/project_report.pdf

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Author

Inés García de la Peña Marco Computational Omics Analysis