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

arXiv: https://arxiv.org/abs/1407.1507 · HTML: https://mlsanity.com/paper/1407.1507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2014.

## Built on by (most cited first, 1 of 1)

- [ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation](https://mlsanity.com/paper/2304.02891/) (2023)

## Abstract (arXiv)

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