Comprehensive benchmarking of somatic mutation detection by the SMaHT Network.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Somatic mosaicism is a key aspect of human biology.
• Detecting somatic mutations is technically challenging.

Purpose of the Study:

• To benchmark sequencing technologies, experimental approaches, and computational methods for somatic mutation detection.
• To establish optimal strategies for comprehensive somatic mutation discovery and analysis.

Main Methods:

• Conducted four large-scale benchmarking experiments.
• Utilized high-coverage short-read (1,000×) and long-read (100-400×) sequencing.
• Integrated bulk, single-cell, and duplex sequencing analyses.
• Employed donor-specific assemblies and human pangenome for improved variant calling.

Main Results:

• Defined optimal strategies for integrating bulk short- and long-read sequencing.
• Demonstrated improved variant calling and extended mutation catalogs to challenging genomic regions.
• Showcased single-cell sequencing's ability to resolve cell type-specific mutational patterns.
• Confirmed that bulk, single-cell, and duplex analyses are complementary for comprehensive mosaicism characterization.

Conclusions:

• Leveraging multiple sequencing approaches (bulk, single-cell, duplex) provides a comprehensive characterization of tissue mosaicism.
• These findings offer a roadmap for accurate, genome-wide somatic mutation discovery and analysis.