Informed and automated k-mer size selection for genome assembly.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• De Bruijn graph genome assembly relies on the parameter k, which involves complex trade-offs.
• Current methods lack automated tools for optimal k-value selection and efficient k-mer abundance histogram generation.

Purpose of the Study:

• To develop an automated tool for estimating the optimal k-mer size in de Bruijn graph genome assembly.
• To provide a fast method for generating k-mer abundance histograms to aid user decision-making.

Main Methods:

• Developed a fast and accurate sampling method for constructing approximate k-mer abundance histograms.
• Implemented a heuristic algorithm utilizing these histograms to estimate the optimal k-value.

Main Results:

• Achieved several orders of magnitude performance improvement over traditional histogram generation methods.
• Demonstrated that the selected k-values using the heuristic lead to high-quality genome assemblies across diverse datasets.

Conclusions:

• KmerGenie offers an efficient and effective solution for optimizing the k-mer parameter in genome assembly.
• The tool facilitates informed decisions by providing rapid k-mer abundance insights, enhancing assembly accuracy.