Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation...

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Nanopore sequencing offers direct DNA/RNA analysis without PCR but faces challenges in hotspot mutation detection.
• Current limitations in nanopore sequencing data analysis hinder its clinical application compared to next-generation sequencing (NGS).

Purpose of the Study:

• To develop a novel data analysis framework, Nano2NGS-Muta, for accurate hotspot mutation detection using nanopore sequencing long reads.
• To bridge the analytical gap between long-read nanopore sequencing and established short-read NGS pipelines.

Main Methods:

• Developed Nano2NGS-Muta, a framework that converts nanopore long reads into short reads.
• Integrated statistical methods with existing NGS analysis pipelines for hotspot mutation detection.
• Applied the framework to analyze nanopore sequencing data for mutation identification.

Main Results:

• Nano2NGS-Muta effectively mitigates false positives/negatives from nanopore sequencing errors and indels.
• Demonstrated improved accuracy in hotspot mutation detection compared to conventional nanopore data analysis methods.
• Successfully adapted nanopore data for NGS-like analysis pipelines.

Conclusions:

• Nano2NGS-Muta provides a robust method for hotspot mutation detection from nanopore sequencing data.
• The framework enhances the reliability and scope of nanopore sequencing in scientific research and clinical practice.
• Facilitates broader adoption of nanopore technology by overcoming data analysis barriers.