Counting the frequencies of k-mers in read libraries is often a first step in the analysis of high-throughput sequencing experiments. Infrequent kmers are assumed to be a result of sequencing errors. The frequent k-mers constitute a reduced but error-free representation of the experiment, which can inform read error correction or serve as the input to de novo assembly methods. Ideally, the memory requirement for counting should be linear in the number of frequent k-mers and not in the, typically much larger, total number of k-mers in the read library. We present a novel method that balances time, space and accuracy requirements to efficiently extract frequent k-mers even for high coverage libraries and large genomes such as human. Our method is designed to minimize cache-misses in a cache-efficient manner by using a Patternblocked Bloom filter to remove infrequent k-mers from consideration in combination with a novel sort-and-compact scheme, instead of a Hash, for the actual counting. While this increases theoretical complexity, the savings in cache misses reduce the empirical running times. A variant can resort to a counting Bloom filter for even larger savings in memory at the expense of false negatives in addition to the false positives common to all Bloom filter based approaches. A comparison to 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 to size 64. The tools are freely available for download at
Turtle REF replaces a standard Bloom filter by a cache-efficient counterpart.
Turtle: Identifying frequent k-mers with cache-efficient algorithms
ArXiv
ArXiv
Biology Computer Science Medicine