GWASKit-POS-to-RSID-Converter-Python-Version-

This is a lightweight Python script that replicates the POS-to-RSID conversion feature from GWASKit, adapted for environments like Google Colab where RAM is limited.

✨ The script splits data by chromosome to optimize memory usage and handles batch processing of genomic data.

📌 Background

GWASKit is a powerful Linux-based toolset for genome-wide association studies (GWAS) data processing. However, its native POS-to-RSID conversion feature requires a Linux system and large memory resources.

This project re-implements that feature in Python, making it accessible for Google Colab and similar platforms with limited RAM.

🚀 Features

🧬 Convert chromosome positions (CHR:POS) to rs IDs.

🗂️ Split large datasets by chromosome to manage memory efficiently.

🔁 Batch processing of multiple files automatically.

🛠️ Designed for low-RAM environments (e.g., Colab).

📚 How to use this pipeline

1. Install GWASkit:

!wget https://github.com/Li-OmicsLab-MPU/GWASkit/releases/download/v1.0.0/GWASkit-1.0.0-amd64_linux.zip
!unzip GWASkit-1.0.0-amd64_linux.zip
!%cd GWASkit-1.0.0/
!chmod a+x ./GWASkit

2. Download the GRCh37/38 rsdb:

!wget https://figshare.com/ndownloader/files/48635851 -O GRCh37.zip
!unzip GRCh37.zip

3. Download or clone this repository:

#Clone the repository
git clone https://github.com/https://github.com/Ali-mhm/GWASKit-posrs.py
cd GWASKit-posrs.py

4. Example usage: 4.1. Define the column mapping for your data (this can be customized based on your data's column names)

from pos2rs_wrapper import pos2rs_pipeline

column_mapping = {
   'chromosome': 'chr',         # Name of the chromosome column
   'position': 'pos',           # Name of the base-pair position column
   'reference': 'ref',          # Name of the reference allele column
   'alternate': 'alt'           # Name of the alternate allele column
}

4.2. Run the pipeline

pos2rs_pipeline(
    input_file='your_data.csv',                    # Input CSV file
    gwaskit_path='/content/GWASkit-1.0.0/GWASkit',  # Path to GWASkit executable
    rsdb_path='/content/GRCh37',                    # Path to rsdb (GRCh37)
    column_mapping=column_mapping,                  # Column name mapping
    output_filename='final_output.csv'              # Optional: specify output file name
)

This will:

1. Split your data by chromosomes

2. Convert position to RSID

3. Merge all results into one CSV file!

✨ Credits

Built using GWASkit by Li-OmicsLab(https://github.com/Li-OmicsLab).