DSK: k-mer counting with very low memory usage
We present a new streaming algorithm for k-mer counting, called DSK (diskstreaming 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 hours. DSK can replace a popular k-mer counting software (Jellyfish) on small-memory servers.
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