Kcollections: A Fast and Efficient Library for K-mers

K-mers form the backbone of many bioinformatic algorithms. They are, however, difficult to store and use efficiently because the number of k-mers increases exponentially as $k$ increases. Many algorithms exist for compressed storage of kmers but suffer from slow insert times or are probabilistic resulting in false-positive k-mers. Furthermore, k-mer libraries usually specialize in associating specific values with k-mers such as a color in colored de Bruijn Graphs or k-mer count. We present kcollections1, a compressed and parallel data structure designed for k-mers generated from whole, assembled genomes. Kcollections is available for $\mathrm {C}++$ and provides set-and maplike structures as well as a k-mer counting data structure all of which utilize parallel operations designed using a MapReduce paradigm. Additionally, we provide basic Python bindings for rapid prototyping. Kcollections makes developing bioinformatic algorithms simpler by abstracting away the tedious task of storing k-mers.1https://www.github.com/masakistan/kcollections