MAGERI: Computational pipeline for molecular-barcoded targeted resequencing.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Unique molecular identifiers (UMIs) are crucial for high-throughput resequencing, enabling accurate rare variant identification in complex DNA.
• Applications span cancer diagnostics and other fields, necessitating specialized bioinformatics tools.
• Existing methods face challenges in handling UMI-based data caveats.

Purpose of the Study:

• Introduce MAGERI, a computational pipeline for UMI-based sequencing data analysis.
• Enable high-fidelity mutation profiling and the reliable calling of ultra-rare variants.
• Provide a robust and efficient solution for UMI data analysis.

Main Methods:

• Developed a computational pipeline named MAGERI.
• Utilized extensive benchmark datasets, including gold-standard samples, tumor cell-free DNA, and public UMI-encoded data.
• Validated performance across different sample types and UMI-based protocols.

Main Results:

• MAGERI demonstrates robust and efficient performance in calling rare variants.
• Achieved high-fidelity mutation profiles from UMI-encoded sequencing data.
• Successfully analyzed both tumor DNA and viral RNA samples.

Conclusions:

• MAGERI is a versatile and accurate tool for UMI-based sequencing analysis.
• The pipeline effectively addresses UMI data complexities for rare variant detection.
• MAGERI supports diverse applications in genomics and diagnostics.