Meti2000-ke/ethiopia-tb-genomic-data-curation-ahri-project

A reproducible bioinformatics pipeline for quality control, trimming, and curation of Mycobacterium tuberculosis sequencing data from Ethiopian studies (AHRI project). # 🧬 Ethiopia TB Genomic Data Curation Pipeline (AHRI Project) This project presents a reproducible bioinformatics workflow for the curation and preprocessing of *Mycobacterium tuberculosis* sequencing data associated with the Armauer Hansen Research Institute (AHRI) project in Ethiopia. ## 📌 Project Overview The goal of this project is to transform raw sequencing data (FASTQ files) into a clean, structured, and analysis-ready dataset through: - Quality control (QC) - Read trimming and filtering - Metadata organization - Data validation - Summary reporting This pipeline ensures reproducibility and provides a strong foundation for downstream genomic analysis such as alignment, variant calling, and lineage identification. ## 🧪 Data Source - Sequencing data obtained from publicly available studies (SRA) - Samples correspond to *Mycobacterium tuberculosis* isolates from Ethiopia - Curated in the context of an AHRI-related research project - Sample identifiers (SRR IDs) are listed in the metadata file ## ⚙️ Pipeline Steps ### 1. Quality Control (Raw Data) - Tool: **FastQC** - Output: `qc_raw/` ### 2. Quality Summary - Tool: **MultiQC** - Aggregates QC reports ### 3. Read Trimming - Tool: **Trimmomatic** - Removes low-quality bases and short reads ### 4. Post-trimming QC - Tool: **FastQC + MultiQC** - Ensures data quality improvement ### 5. Metadata Curation - Structured sample information stored in `metadata.csv` ### 6. Data Validation - Python script verifies: - Missing values - File consistency - Sample integrity ### 7. Summary Statistics - Tool: **SeqKit** - Reports sequence-level statistics ## 🚀 How to Run the Pipeline ### 1. Clone the repository git clone github.com