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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

Fast and Accurate Algorithms for Mapping and Aligning Long Reads.

Wen Yang¹, Lusheng Wang^1,2

¹Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|June 23, 2021

Summary

New algorithms improve long-read DNA sequence analysis by enhancing mapping and local alignment. The mapAlign software achieves higher accuracy and speed for Nanopore and SMRT sequencing data, aiding structural variant identification and repetitive region analysis.

Keywords:

k-mer-based local alignment with variable value of k long-read mapping and long-read local alignment longest common subsequence with distance constraints

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Short sequencing reads limit DNA analysis tasks like variant identification and phasing.
Longer reads are crucial for overcoming these limitations in complex genomic regions.

Purpose of the Study:

To develop novel algorithms for accurate long-read mapping to reference genomes.
To create efficient heuristic algorithms for local alignment of long reads.

Main Methods:

Formulated read mapping as a longest common subsequence problem with distance constraints.
Developed a recursive k-mer alignment heuristic for local alignments.
Implemented algorithms in C++ into the mapAlign software package.

Main Results:

mapAlign significantly improves alignment identity and scores for Nanopore and SMRT data.
Achieved 91.53% (Nanopore) and 85.36% (SMRT) letter alignment to reference genomes.
Outperformed the best-known methods in accuracy and processing speed.

Conclusions:

The proposed mapAlign algorithms offer superior performance for long-read DNA sequence analysis.
This advancement facilitates more accurate identification of structural variants and analysis of repetitive genomic regions.
The software package provides a faster and more accurate solution for genomic data processing.