  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.
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