Scalable RNA-seq Analysis Framework

A reproducible, cloud-ready RNA-seq workflow built with object-oriented Python. Automates alignment, expression quantification, and QC validation across large multi-sample cohorts.

Overview

Raw FASTQ → Trimming → Alignment → Quantification → QC Report → Expression Matrix

Key Results:

• 28% reduction in analysis turnaround time
• Automated Pytest-driven QC validation
• AWS-deployed for parallel multi-sample processing

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Repository Structure

rnaseq-framework/
├── data/
│   ├── raw/                      # Raw FASTQ files
│   ├── trimmed/                  # Post-trimming reads
│   ├── aligned/                  # BAM files
│   └── counts/                   # Gene count matrices
├── rnaseq/
│   ├── __init__.py
│   ├── trimmer.py                # Read trimming module
│   ├── aligner.py                # STAR/HISAT2 alignment wrapper
│   ├── quantifier.py             # Expression quantification (featureCounts/HTSeq)
│   ├── qc_metrics.py             # QC metric aggregation
│   └── report_generator.py       # Automated QC report output
├── tests/
│   ├── test_aligner.py
│   ├── test_quantifier.py
│   └── test_qc_metrics.py
├── scripts/
│   └── run_rnaseq.sh             # Bash pipeline entry point
├── aws/
│   └── batch_job_definition.json # AWS Batch config for parallel runs
├── config/
│   └── config.yaml
├── requirements.txt
└── README.md

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Quickstart

git clone https://github.com/yourusername/rnaseq-framework.git
cd rnaseq-framework

pip install -r requirements.txt

# Run on a single sample
python -m rnaseq.aligner --input data/raw/sample1.fastq --output data/aligned/

# Run full pipeline
bash scripts/run_rnaseq.sh --samples sample_sheet.csv --ref /path/to/genome

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Tech Stack

Component	Technology
Language	Python 3.10+ (OOP)
Alignment	STAR / HISAT2
Quantification	featureCounts / HTSeq
QC	MultiQC, FastQC
Testing	Pytest
Cloud	AWS (S3, Batch)

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Running Tests

pytest tests/ -v --tb=short

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License

MIT License