  Genome assembly tools based on the de Bruijn graph framework rely on a
parameter k, which represents a trade-off between several competing effects
that are difficult to quantify. There is currently a lack of tools that would
automatically estimate the best k to use and/or quickly generate histograms of
k-mer abundances that would allow the user to make an informed decision.
  We develop a fast and accurate sampling method that constructs approximate
abundance histograms with a several orders of magnitude performance improvement
over traditional methods. We then present a fast heuristic that uses the
generated abundance histograms for putative k values to estimate the best
possible value of k. We test the effectiveness of our tool using diverse
sequencing datasets and find that its choice of k leads to some of the best
assemblies.
  Our tool KmerGenie is freely available at: http://kmergenie.bx.psu.edu/

Genomics