MGDB: A Novel Bioinformatics Quality Control Tool for Clinical Next-Generation Sequencing

Area of Science:

• Bioinformatics
• Clinical Diagnostics
• Genomic Medicine

Background:

• Next-generation sequencing (NGS) is revolutionizing clinical diagnostics for genetic variation detection.
• Stringent quality control is essential for the successful implementation of NGS workflows.
• Existing methods may lack the precision needed for comprehensive quality assessment in clinical settings.

Purpose of the Study:

• Introduce Molecular Genetics Dashboard (MGDB), a novel bioinformatics tool.
• Enhance quality control (QC) in clinical next-generation sequencing (NGS) workflows.
• Improve the accuracy and reliability of genetic variation detection.

Main Methods:

• Developed a variant-level monitoring tool using Python's Dash framework and MySQL databases.
• Utilized Docker-compose for containerization, ensuring portability and ease of deployment.
• Enabled flexible inclusion/exclusion of samples and interpreter notes for customized analysis.

Main Results:

• MGDB provides interactive, variant-level run-to-run monitoring for consistent variant detection.
• The tool facilitates visualization and assessment of variant data, identifying inconsistencies.
• Demonstrated improved data management and interpretation compared to traditional quality control methods.

Conclusions:

• MGDB offers a robust, user-friendly solution for enhancing clinical NGS quality control.
• The tool contributes to greater accuracy and reliability in genetic variant detection.
• MGDB is freely available on GitHub for broader adoption and use.