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https://github.com/se7en69/rna-seq-data-processing-and-analysis-pipeline

This pipeline automates essential steps for RNA-Seq data analysis, including quality control, read trimming, alignment to a reference genome, and coverage quantification. It leverages tools like FastQC, fastp, STAR, and bedtools to ensure high-quality results, with MultiQC reports providing an overview at each stage.
https://github.com/se7en69/rna-seq-data-processing-and-analysis-pipeline

bioinformaitcs-scripting bioinformatics bioinformatics-pipeline data-analysis linux scripts shell

Last synced: 16 days ago
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Host: GitHub
URL: https://github.com/se7en69/rna-seq-data-processing-and-analysis-pipeline
Owner: se7en69
License: mit
Created: 2024-10-16T10:50:00.000Z (about 1 month ago)
Default Branch: main
Last Pushed: 2024-10-16T11:00:38.000Z (about 1 month ago)
Last Synced: 2024-10-31T05:05:29.474Z (16 days ago)
Topics: bioinformaitcs-scripting, bioinformatics, bioinformatics-pipeline, data-analysis, linux, scripts, shell
Language: Shell
Homepage:
Size: 13.7 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# RNA-Seq Data Processing and Analysis Pipeline

This project provides a comprehensive and modular RNA-Seq data processing pipeline, automating quality control, read trimming, alignment, and coverage quantification. The pipeline ensures efficient, reproducible RNA-Seq analysis using popular bioinformatics tools, with detailed logging and reporting at every stage.

---

## 🚀 **Features**
- **Automated Quality Control**: Pre- and post-trimming checks using FastQC with compiled MultiQC reports.
- **Read Trimming**: Cleans raw reads by removing low-quality bases and adapters using `fastp`.
- **Alignment**: Aligns cleaned reads to the reference genome with the STAR aligner, producing BAM files.
- **Coverage Quantification**: Measures coverage across defined regions using `bedtools`.
- **Detailed Logs and Error Handling**: Each script generates logs, ensuring easy debugging and tracking.
- **Modular Design**: Each step can be run independently, supporting flexible workflows.

---

## 🛠️ **Tools and Technologies Used**
- **FastQC**: For quality control of raw and trimmed reads.
- **fastp**: Trimming low-quality regions and adapters.
- **STAR**: Alignment of RNA-Seq reads to the reference genome.
- **bedtools**: For calculating coverage over specific regions.
- **MultiQC**: Compiling QC reports into a single summary.
- **Bash scripting**: For automation of tasks across the pipeline.

---

## 📂 **Pipeline Overview**

1. **Quality Control of Raw Reads**
- Run FastQC on the input FASTQ files to assess initial quality.
- Example command:
```bash
./1-quality_control.sh -d /path/to/raw_fastq/ -o /path/to/qc_reports/ -t 8
```

2. **Read Trimming**
- Use `fastp` to remove adapters and low-quality bases.
- Example command:
```bash
./2-trimming_reads.sh -d /path/to/downsampled_fastq/ -o /path/to/trimmed_fastq/ -q /path/to/qc/ -t 8
```

3. **Quality Control on Trimmed Reads**
- Re-run FastQC on trimmed reads to ensure quality improvements.
- Example command:
```bash
./3-quality_control_trimmed.sh -d /path/to/trimmed_fastq/ -o /path/to/trimmed_qc_reports/ -t 8
```

4. **Compile Quality Control Reports**
- Generate a single summary report using MultiQC.
- Example command:
```bash
./4-compile_qc_reports.sh -d /path/to/trimmed_qc_reports/ -o /path/to/multiqc_report/
```

5. **Alignment to Reference Genome**
- Use STAR to align reads to a reference genome and generate BAM files.
- Example command:
```bash
./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
```

6. **Coverage Quantification**
- Calculate coverage over specific genomic regions using bedtools.
- Example command:
```bash
./6-quantification_coverage.sh -b /path/to/bam_files/ -r /path/to/regions.bed -o /path/to/coverage_output/ -t 8
```

---

## 💻 **How to Run the Pipeline**

### 1. **Clone the Repository**
```bash
git clone https://github.com/your-username/rna-seq-pipeline.git
cd rna-seq-pipeline
```

### 2. **Install Dependencies**
Make sure the following tools are installed and available in your `PATH`:
- `FastQC`
- `fastp`
- `STAR`
- `bedtools`
- `MultiQC`

Use `conda` or your package manager to install these tools if needed:
```bash
conda install -c bioconda fastqc fastp star bedtools multiqc
```

### 3. **Prepare Input Data**
- Place your raw FASTQ files, reference genome, and annotation files in the appropriate directories.

### 4. **Execute the Pipeline**
- Run each script in order or as needed (refer to the **Pipeline Overview** section).
- Logs will be generated for each step to monitor progress and errors.

---

## ⚙️ **Pipeline Dependencies**
- **Operating System**: Linux/macOS (Bash shell environment)
- **Memory**: Recommended 16 GB RAM or more for large datasets
- **CPU**: Multi-threading support (adjust `-t` argument for number of threads)

---

## 📝 **Logging and Troubleshooting**
Each script generates detailed log files in the working directory:
- Example log: `QualityControl_log.out`
- Errors are captured separately for easier troubleshooting: `error.log`

---

## 📊 **Example Use Case**
This pipeline can be used to process RNA-Seq data from experiments studying gene expression changes, alternative splicing, or differential expression between conditions.

---

## 🤝 **Contributing**
Contributions are welcome! Please submit a pull request or open an issue if you find bugs or have feature requests.

---

## 📄 **License**
This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.

---

With this pipeline, you can ensure efficient and reproducible RNA-Seq data processing, enabling better insights from high-throughput sequencing data.