title: DSK: k-mer counting with very low memory usage.
abstract: CONCLUSIONS
  Counting all the k-mers (substrings of length k) in DNA/RNA sequencing reads is the preliminary step of many bioinformatics applications. However, state of the art k-mer counting methods require that a large data structure resides in memory. Such structure typically grows with the number of distinct k-mers to count. We present a new streaming algorithm for k-mer counting, called DSK (disk streaming of k-mers), which only requires a fixed user-defined amount of memory and disk space. This approach realizes a memory, time and disk trade-off. The multi-set of all k-mers present in the reads is partitioned, and partitions are saved to disk. Then, each partition is separately loaded in memory in a temporary hash table. The k-mer counts are returned by traversing each hash table. Low-abundance k-mers are optionally filtered. DSK is the first approach that is able to count all the 27-mers of a human genome dataset using only 4.0 GB of memory and moderate disk space (160 GB), in 17.9 h. DSK can replace a popular k-mer counting software (Jellyfish) on small-memory servers.
  
  
  BACKGROUND
  http://minia.genouest.org/dsk
authors: Algorizk, 75013 Paris, France.
authors: Guillaume
authors: G
authors: Rizk
authors: Dominique
authors: D
authors: Lavenier
authors: Rayan
authors: R
authors: Chikhi
mesh_terms: D000465:Algorithms; D015894:Genome, Human; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software