title: Gerbil: a fast and memory-efficientk-mer counter with GPU-support.
abstract: BACKGROUND
  A basic task in bioinformatics is the counting ofk-mers in genome sequences. Existingk-mer counting tools are most often optimized for smallk< 32 and suffer from excessive memory resource consumption or degrading performance for largek. However, given the technology trend towards long reads of next-generation sequencers, support for largekbecomes increasingly important.
  
  
  RESULTS
  We present the open sourcek-mer counting softwareGerbilthat has been designed for the efficient counting ofk-mers fork≥ 32. Our software is the result of an intensive process of algorithm engineering. It implements a two-step approach. In the first step, genome reads are loaded from disk and redistributed to temporary files. In a second step, thek-mers of each temporary file are counted via a hash table approach. In addition to its basic functionality,Gerbilcan optionally use GPUs to accelerate the counting step. In a set of experiments with real-world genome data sets, we show thatGerbilis able to efficiently support both small and largek.
  
  
  CONCLUSIONS
  WhileGerbil's performance is comparable to existing state-of-the-art open sourcek-mer counting tools for smallk< 32, it vastly outperforms its competitors for largek, thereby enabling new applications which require large values ofk.
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saae), Germany.
authors.forename: Marius
authors.initials: M
authors.lastname: Erbert
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saae), Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Rechner
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saae), Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Müller-Hannemann
keywords: Algorithm engineering; GPU computing; Genome sequences; de novo assembly; k-mer counting