{"id":18062075,"url":"https://github.com/se7en69/rna-seq-data-processing-and-analysis-pipeline","last_synced_at":"2026-05-02T19:31:48.505Z","repository":{"id":258166308,"uuid":"873544492","full_name":"se7en69/RNA-Seq-Data-Processing-and-Analysis-Pipeline","owner":"se7en69","description":"This pipeline automates essential steps for RNA-Seq data analysis, including quality control, read trimming, alignment to a reference genome, and coverage quantification. 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The pipeline ensures efficient, reproducible RNA-Seq analysis using popular bioinformatics tools, with detailed logging and reporting at every stage.\n\n---\n\n## 🚀 **Features**  \n- **Automated Quality Control**: Pre- and post-trimming checks using FastQC with compiled MultiQC reports.  \n- **Read Trimming**: Cleans raw reads by removing low-quality bases and adapters using `fastp`.  \n- **Alignment**: Aligns cleaned reads to the reference genome with the STAR aligner, producing BAM files.  \n- **Coverage Quantification**: Measures coverage across defined regions using `bedtools`.  \n- **Detailed Logs and Error Handling**: Each script generates logs, ensuring easy debugging and tracking.  \n- **Modular Design**: Each step can be run independently, supporting flexible workflows.\n\n---\n\n## 🛠️ **Tools and Technologies Used**  \n- **FastQC**: For quality control of raw and trimmed reads.  \n- **fastp**: Trimming low-quality regions and adapters.  \n- **STAR**: Alignment of RNA-Seq reads to the reference genome.  \n- **bedtools**: For calculating coverage over specific regions.  \n- **MultiQC**: Compiling QC reports into a single summary.  \n- **Bash scripting**: For automation of tasks across the pipeline.\n\n---\n\n## 📂 **Pipeline Overview**\n\n1. **Quality Control of Raw Reads**  \n   - Run FastQC on the input FASTQ files to assess initial quality.  \n   - Example command:  \n     ```bash\n     ./1-quality_control.sh -d /path/to/raw_fastq/ -o /path/to/qc_reports/ -t 8\n     ```\n   \n2. **Read Trimming**  \n   - Use `fastp` to remove adapters and low-quality bases.  \n   - Example command:  \n     ```bash\n     ./2-trimming_reads.sh -d /path/to/downsampled_fastq/ -o /path/to/trimmed_fastq/ -q /path/to/qc/ -t 8\n     ```\n\n3. **Quality Control on Trimmed Reads**  \n   - Re-run FastQC on trimmed reads to ensure quality improvements.  \n   - Example command:  \n     ```bash\n     ./3-quality_control_trimmed.sh -d /path/to/trimmed_fastq/ -o /path/to/trimmed_qc_reports/ -t 8\n     ```\n\n4. **Compile Quality Control Reports**  \n   - Generate a single summary report using MultiQC.  \n   - Example command:  \n     ```bash\n     ./4-compile_qc_reports.sh -d /path/to/trimmed_qc_reports/ -o /path/to/multiqc_report/\n     ```\n\n5. **Alignment to Reference Genome**  \n   - Use STAR to align reads to a reference genome and generate BAM files.  \n   - Example command:  \n     ```bash\n     ./5-alignment_star.sh -g /path/to/genome/ -i /path/to/trimmed_fastq/ -o /path/to/aligned_bam/ -a /path/to/annotation.gtf -t 8\n     ```\n\n6. **Coverage Quantification**  \n   - Calculate coverage over specific genomic regions using bedtools.  \n   - Example command:  \n     ```bash\n     ./6-quantification_coverage.sh -b /path/to/bam_files/ -r /path/to/regions.bed -o /path/to/coverage_output/ -t 8\n     ```\n\n---\n\n## 💻 **How to Run the Pipeline**\n\n### 1. **Clone the Repository**  \n```bash\ngit clone https://github.com/your-username/rna-seq-pipeline.git\ncd rna-seq-pipeline\n```\n\n### 2. **Install Dependencies**  \nMake sure the following tools are installed and available in your `PATH`:  \n- `FastQC`  \n- `fastp`  \n- `STAR`  \n- `bedtools`  \n- `MultiQC`  \n\nUse `conda` or your package manager to install these tools if needed:\n```bash\nconda install -c bioconda fastqc fastp star bedtools multiqc\n```\n\n### 3. **Prepare Input Data**  \n- Place your raw FASTQ files, reference genome, and annotation files in the appropriate directories.\n\n### 4. **Execute the Pipeline**  \n- Run each script in order or as needed (refer to the **Pipeline Overview** section).  \n- Logs will be generated for each step to monitor progress and errors.\n\n---\n\n## ⚙️ **Pipeline Dependencies**  \n- **Operating System**: Linux/macOS (Bash shell environment)  \n- **Memory**: Recommended 16 GB RAM or more for large datasets  \n- **CPU**: Multi-threading support (adjust `-t` argument for number of threads)\n\n---\n\n## 📝 **Logging and Troubleshooting**  \nEach script generates detailed log files in the working directory:\n- Example log: `QualityControl_log.out`\n- Errors are captured separately for easier troubleshooting: `error.log`\n\n---\n\n## 📊 **Example Use Case**  \nThis pipeline can be used to process RNA-Seq data from experiments studying gene expression changes, alternative splicing, or differential expression between conditions.\n\n---\n\n## 🤝 **Contributing**  \nContributions are welcome! Please submit a pull request or open an issue if you find bugs or have feature requests.\n\n---\n\n## 📄 **License**  \nThis project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.\n\n---\n\nWith this pipeline, you can ensure efficient and reproducible RNA-Seq data processing, enabling better insights from high-throughput sequencing data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse7en69%2Frna-seq-data-processing-and-analysis-pipeline","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fse7en69%2Frna-seq-data-processing-and-analysis-pipeline","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse7en69%2Frna-seq-data-processing-and-analysis-pipeline/lists"}