Ultrasensitive detection of rare mutations using next-generation targeted resequencing

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Next-generation sequencing (NGS) enables deep interrogation of genomic regions.
• Detecting rare mutations in heterogeneous samples is challenged by sequencing errors and algorithm limitations.
• Accurate variant calling requires high statistical power and low false positive rates.

Purpose of the Study:

• To develop and validate a robust method for detecting rare mutations at 0.1% fractional representation.
• To improve the sensitivity and specificity of mutation detection in clinical samples.
• To apply the method for identifying antiviral resistance mutations in H1N1 influenza.

Main Methods:

• Integration of a high-accuracy indexing strategy and reference replication for error variance estimation.
• Application of a statistical model to estimate error rates and quantify variant fractions.
• Validation using synthetic DNA admixtures and analysis of H1N1 influenza clinical samples.

Main Results:

• Robust detection of mutations at 0.1% fractional representation (1 in 1000 wild-type alleles).
• Method demonstrated high specificity (99%) and sensitivity (100%) in validation studies.
• Identified an oseltamivir resistance mutation in H1N1 influenza neuraminidase gene at 0.18% fractional representation.

Conclusions:

• The developed method significantly enhances the ability to detect rare mutations in clinical samples.
• This technique holds promise for identifying low-frequency variants, including drug resistance mutations.
• The approach provides a powerful tool for genomic analysis in diverse biological and clinical settings.