SKESA: strategic k-mer extension for scrupulous assemblies

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• De novo genome assembly is crucial for understanding microbial genomes.
• Existing assemblers face challenges with read quality, contamination, and reproducibility.
• Efficient assembly tools are needed for large-scale sequencing projects.

Purpose of the Study:

• To introduce SKESA, a novel DeBruijn graph-based assembler for microbial genomes.
• To evaluate SKESA's performance against established assemblers like SPAdes and MegaHit.
• To highlight SKESA's capabilities in handling contamination and ensuring assembly consistency.

Main Methods:

• SKESA utilizes a DeBruijn graph approach for sequence assembly.
• Comparative analysis was performed using Illumina sequencing reads from microbial genomes.
• Performance metrics included assembly quality, contiguity, speed, and reproducibility.

Main Results:

• SKESA demonstrates superior or comparable sequence quality and contiguity to SPAdes and MegaHit.
• The assembler effectively manages low-level contamination present in sequencing reads.
• SKESA provides identical assembly outputs across multiple runs, irrespective of computational resources.

Conclusions:

• SKESA is a robust and efficient tool for de novo assembly of microbial genomes.
• Its speed, accuracy, and reproducibility make it suitable for large-scale applications, including pathogen detection.
• SKESA is publicly available, promoting its adoption in the research community.