KMC 2: Fast and resource-frugal $k$-mer counting

TL;DR

KMC 2 introduces a fast, memory-efficient disk-based method for $k$-mer counting in large genomic datasets, outperforming existing tools in speed while using moderate RAM.

Contribution

The paper presents a novel $k$-mer counting algorithm that significantly improves speed and reduces memory usage by employing signatures and parallel processing.

Findings

At least twice as fast as competitors like Jellyfish 2 and KMC 1.

Counts 28-mers in a human dataset in about 20 minutes on a standard PC.

Uses about 12 GB or less RAM, making it resource-frugal.

Abstract

Motivation: Building the histogram of occurrences of every $k$-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of $k$-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for $k$-mer counting, preferably using moderate amounts of memory. Results: We present a novel method for $k$-mer counting, on large datasets at least twice faster than the strongest competitors (Jellyfish~2, KMC~1), using about 12\,GB (or less) of RAM memory. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using $(k, x)$-mers allows to significantly reduce the I/O, and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC~2 allows to count the 28-mers of a human reads collection with 44-fold coverage (106\,GB of compressed size) in about 20 minutes, on a 6-core Intel i7 PC with an SSD. Availability: KMC~2 is freely available at http://sun.aei.polsl.pl/kmc. Contact: sebastian.deorowicz@polsl.pl