KCMBT: a k-mer Counter based on Multiple Burst Trees.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Bioinformatics applications frequently require counting k-length substrings (k-mers) in large genome sequences.
• Efficient k-mer counting is crucial for tasks like genome assembly, repeat detection, and error correction.

Purpose of the Study:

• To introduce a novel and efficient trie-based algorithm for k-mer counting.
• To evaluate the performance of the proposed algorithm against existing state-of-the-art methods.

Main Methods:

• Development of a novel trie-based algorithm named k-mer Counter based on Multiple Burst Trees (KCMBT).
• Comparative performance analysis of KCMBT against established k-mer counting algorithms (KMC2, Jellyfish2) using benchmark datasets.

Main Results:

• KCMBT demonstrates a 20-30% speed improvement over KMC2 across five benchmark datasets when using multiple threads.
• KCMBT is approximately 30% faster than KMC2 on a human genome dataset.
• KCMBT significantly outperforms Jellyfish2, achieving speeds up to six times faster.

Conclusions:

• The proposed KCMBT algorithm offers a substantial performance enhancement for k-mer counting in bioinformatics.
• KCMBT provides a faster and more efficient solution for handling large-scale genomic data analysis.