Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Genome diagnostics are crucial in healthcare, with targeted sequencing offering a focused approach for genetic analysis.
• Nanopore sequencing is valuable for targeted sequencing due to its portability and long reads, but computational challenges remain in accurately mapping subsequences from its raw signal data, especially at low coverage and accuracy.

Purpose of the Study:

• To address the computational challenges in nanopore-based targeted sequencing.
• To develop novel algorithms for efficient and accurate signal-based subsequence searching in nanopore reads.
• To enable reliable SNP detection and haplotype classification from low-quality nanopore data.

Main Methods:

• Developed a novel signal-based subsequence inquiry pipeline utilizing dynamic time warping on raw electrical current signals.
• Introduced two new algorithms that operate directly on signals, bypassing base-calling for potentially higher accuracy.
• Implemented novel criteria for signal quality analysis and data classification.

Main Results:

• Demonstrated the efficiency and effectiveness of the proposed algorithms through comprehensive experiments on real-world nanopore datasets.
• Successfully applied the algorithms to SNP detection under low sequencing coverage.
• Showcased the utility of the algorithms for haplotype classification with low sequencing accuracy.

Conclusions:

• The developed signal-based algorithms offer a robust solution for subsequence inquiry in nanopore targeted sequencing.
• These methods enhance the reliability of genetic analysis, particularly in challenging scenarios like low coverage and accuracy.
• The pipeline provides a valuable tool for advancing clinical applications of nanopore sequencing.