A basic task in bioinformatics is the counting of k-mers in genome strings.
The k-mer counting problem is to build a histogram of all substrings of
length k in a given genome sequence. We present the open source k-mer
counting software Gerbil that has been designed for the efficient counting of
k-mers for k≥32. Given the technology trend towards long reads of
next-generation sequencers, support for large k becomes increasingly
important. While existing k-mer counting tools suffer from excessive memory
resource consumption or degrading performance for large k, Gerbil is able to
efficiently support large k without much loss of performance. Our software
implements a two-disk approach. In the first step, DNA reads are loaded from
disk and distributed to temporary files that are stored at a working disk. In a
second step, the temporary files are read again, split into k-mers and
counted via a hash table approach. In addition, Gerbil can optionally use GPUs
to accelerate the counting step. For large k, we outperform state-of-the-art
open source k-mer counting tools for large genome data sets.Comment: A short version of this paper will appear in the proceedings of WABI
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