MBG: Minimizer-based sparse de Bruijn Graph construction.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• De Bruijn graphs are efficient for analyzing sequencing data but traditionally limited by high error rates in long reads.
• Recent advancements in high-fidelity (HiFi) long-read sequencing offer both length and low error rates, making them suitable for de Bruijn graph applications.

Purpose of the Study:

• To develop an efficient tool for constructing sparse de Bruijn graphs from HiFi reads.
• To enable the application of de Bruijn graph methodologies to long-read sequencing data.

Main Methods:

• Implementation of MBG, a novel software tool for building sparse de Bruijn graphs.
• Utilizing HiFi reads as input for graph construction.

Main Results:

• MBG demonstrates superior performance compared to existing tools for dense de Bruijn graph construction.
• MBG successfully constructed a de Bruijn graph for a 50x human genome dataset in four hours on a single core.
• MBG assembled the E. coli genome into a single contig in just 8 seconds.

Conclusions:

• MBG effectively leverages HiFi reads for de Bruijn graph construction, overcoming previous limitations.
• The tool offers significant speed and efficiency improvements for genome assembly and analysis using long reads.