kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-molecule sequencing (SMS) generates long reads with high potential for genomic applications.
• Current mapping tools struggle with long, noisy SMS reads, often producing partial alignments and missing structural variants.
• Accurate mapping is crucial for downstream genomic analyses.

Purpose of the Study:

• To introduce kngMap, a novel k-mer neighborhood graph-based mapper designed for long, noisy SMS reads.
• To evaluate kngMap's performance against established SMS mapping tools.
• To demonstrate kngMap's ability to improve read alignment sensitivity and span structural variants.

Main Methods:

• Developed kngMap, a C++ based mapper utilizing a k-mer neighborhood graph approach.
• Benchmarked kngMap against ten popular SMS mapping tools using simulated and real-life SMS datasets.
• Assessed mapping sensitivity, base alignment, and ability to span structural variants.

Main Results:

• kngMap demonstrated higher sensitivity, aligning more reads and bases compared to ten other popular SMS mappers.
• kngMap successfully produced consecutive, end-to-end alignments for entire reads.
• The tool effectively spanned various categories of structural variants within the reads.

Conclusions:

• kngMap is a superior tool for mapping long, noisy single-molecule sequencing reads.
• Its enhanced sensitivity and ability to span structural variants facilitate more comprehensive genomic analyses.
• kngMap offers a valuable solution for researchers utilizing advanced sequencing technologies.