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Counting the frequencies of k-mers in read libraries is often a first step inthe analysis of high-throughput sequencing experiments. Infrequent k-mers areassumed to be a result of sequencing errors. The frequent k-mers constitute areduced but error-free representation of the experiment, which can inform readerror correction or serve as the input to de novo assembly methods. Ideally,the memory requirement for counting should be linear in the number of frequentk-mers and not in the, typically much larger, total number of k-mers in theread library.  We present a novel method that balances time, space and accuracy requirementsto efficiently extract frequent k-mers even for high coverage libraries andlarge genomes such as human. Our method is designed to minimize cache-misses ina cache-efficient manner by using a Pattern-blocked Bloom filter to removeinfrequent k-mers from consideration in combination with a novelsort-and-compact scheme, instead of a Hash, for the actual counting. While thisincreases theoretical complexity, the savings in cache misses reduce theempirical running times. A variant can resort to a counting Bloom filter foreven larger savings in memory at the expense of false negatives in addition tothe false positives common to all Bloom filter based approaches. A comparisonto the state-of-the-art shows reduced memory requirements and running times.Note that we also provide the first competitive method to count k-mers up tosize 64.

Turtle: Identifying frequent k-mers with cache-efficient algorithms