CHTKC: a robust and efficient k-mer counting algorithm based on a lock-free chaining hash table.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate k-mer counting is crucial for DNA sequence analysis, including genome assembly and error correction.
• High-throughput sequencing generates massive datasets, posing computational challenges for traditional k-mer counting methods.

Purpose of the Study:

• To propose a robust and efficient method for k-mer counting in large DNA sequences.
• To address memory limitations and optimize performance in bioinformatics applications.

Main Methods:

• Developed CHTKC, a novel k-mer counting algorithm utilizing a lock-free hash table with linked lists for collision resolution.
• Implemented memory optimization mechanisms to manage datasets exceeding available RAM.
• Designed strategies for efficient handling of memory-constrained scenarios.

Main Results:

• CHTKC demonstrated robust performance across seven diverse datasets.
• The method was compared against established tools like Jellyfish2 and KMC3, showing competitive efficiency.
• The hash-table-based approach proved effective for k-mer counting, even with memory constraints.

Conclusions:

• CHTKC offers an efficient and memory-optimized solution for the k-mer counting problem.
• Hash-table-based algorithms remain a viable and powerful approach in modern bioinformatics.
• The developed method contributes to advancing large-scale DNA sequence analysis.