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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

pathMap: a path-based mapping tool for long noisy reads with high sensitivity.

Ze-Gang Wei^1,2, Xiao-Dan Zhang¹, Xing-Guo Fan¹

¹School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China.

Briefings in Bioinformatics

|March 22, 2024

Summary

This summary is machine-generated.

pathMap, a novel k-mer graph-based mapper, enhances single-molecule sequencing (SMS) read mapping sensitivity. It significantly increases mapped chains and bases compared to existing methods, improving pathogen identification.

Keywords:

long noisy reads long-read sequencing read mapping sequence alignment

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Last Updated: Jun 30, 2025

Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: February 24, 2015

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

Single-molecule sequencing (SMS) technologies are rapidly advancing, producing longer reads.
Accurate mapping of these long reads to reference genomes is crucial for downstream genomic analyses.
Current mapping methods face challenges in maximizing sensitivity and accurately aligning long, error-prone reads.

Purpose of the Study:

To introduce pathMap, a novel k-mer graph-based mapper designed for high-sensitivity mapping of SMS reads.
To improve the detection of aligned regions and bases from long sequencing reads.
To enhance the robustness of mapping against sequence errors and improve pathogen identification.

Main Methods:

Developed pathMap, a k-mer graph-based algorithm that models read alignment as a path selection problem.
Implemented an iterative longest path search within the k-mer graph to identify high-quality alignment chains.
Evaluated pathMap's performance against state-of-the-art mappers (minimap2, Winnowmap2) using simulated and real-world datasets.

Main Results:

pathMap achieved at least 11.50% more mapped chains than its closest competitor.
Mapping sensitivity increased by 17.28% (PacBio) and 13.84% (Oxford Nanopore) in terms of aligned bases.
pathMap demonstrated superior robustness to sequence errors and enhanced sensitivity for pathogen identification.

Conclusions:

pathMap offers a significant advancement in mapping sensitivity for single-molecule sequencing reads.
The k-mer graph-based approach effectively identifies more high-quality alignment chains.
pathMap is a valuable tool for genomic analysis, particularly for pathogen detection using long reads.