Fast and memory efficient approach for mapping NGS reads to a reference genome

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Next-generation sequencing (NGS) technologies produce millions of short DNA reads per run.
• Aligning these reads to a reference genome is crucial for applications like genetic variation analysis and genome re-sequencing.
• Existing alignment tools demand substantial memory for reference genome indexing, impacting efficiency.

Purpose of the Study:

• To develop a novel, memory-efficient approach for reference genome indexing and short read alignment.
• To improve the speed and reduce the memory footprint of next-generation sequencing data analysis.

Main Methods:

• Proposed WIT (Wavelet-Tree Indexing) approach utilizing Burrows-Wheeler Transform (BWT) and Wavelet Tree (WT).
• Implemented exact and approximate alignment capabilities.
• Evaluated WIT's performance against established tools like BWA, Subread, Kart, and Minimap2.

Main Results:

• WIT requires significantly less reference genome indexing space (0.6N) compared to existing methods (1.25N to 5N).
• Alignment time using WIT is comparable to other leading tools despite its smaller index size.
• WIT demonstrated superior accuracy and confidentiality compared to Minimap2.

Conclusions:

• WIT offers a highly efficient solution for reference genome indexing and short read alignment in next-generation sequencing.
• The method provides a valuable alternative for large-scale genomic analyses, reducing computational resource requirements.
• Source code for WIT is publicly available for further research and application.