Fig. 1

Overview of the KrakenUniq algorithm and output. a An input read is shown as a long gray rectangle, with k-mers shown as shorter rectangles below it. The taxon mappings for each k-mer are compared to the database, shown as larger rectangles on the right. For each taxon, a unique k-mer counter is instantiated, and the observed k-mers (K7, K8, and K9) are added to the counters. b Unique k-mer counting is implemented with the probabilistic estimation method HyperLogLog (HLL) using 16 KB of memory per counter, which limits the error in the cardinality estimate to 1% (see main text). c The output includes the number of reads, unique k-mers, duplicity (average time each k-mer has been seen), and coverage for each taxon observed in the input data