findere: fast and precise approximate membership query

Approximate membership query (AMQ) structures as Cuckoo filters or Bloom filters are widely used for representing large sets of elements. Their lightweight space usage explains their success, mainly as they are the only way to scale hundreds of billions or trillions of elements. However, they suffer by nature from non-avoidable false-positive calls that bias downstream analyses of methods using these data structures. In this work we propose a simple strategy and its implementation for reducing the false-positive rate of any AMQ data structure indexing k-mers (words of length k). The method we propose, called findere, enables to speed-up the queries by a factor two and to decrease the false-positive rate by two order of magnitudes. This achievement is done one the fly at query time, without modifying the original indexing data-structure, without generating false-negative calls and with no memory overhead. With no drawback, this method, as simple as it is effective, reduces either the false-positive rate or the space required to represent a set given a user-defined false-positive rate.

Sketching and Sublinear Data Structures in Genomics

Large-scale genomics demands computational methods that scale sublinearly with the growth of data. We review several data structures and sketching techniques that have been used in genomic analysis methods. Specifically, we focus on four key ideas that take different approaches to achieve sublinear space usage and processing time: compressed full-text indices, approximate membership query data structures, locality-sensitive hashing, and minimizers schemes. We describe these techniques at a high level and give several representative applications of each.