Compact universal k-mer hitting sets

Yaron Orenstein, David Pellow, Guillaume Marçais, Ron Shamir, Carl Kingsford

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


We address the problem of finding a minimum-size set of k-mers that hits L-long sequences. The problem arises in the design of compact hash functions and other data structures for efficient handling of large sequencing datasets. We prove that the problem of hitting a given set of L-long sequences is NP-hard and give a heuristic solution that finds a compact universal k-mer set that hits any set of L-long sequences. The algorithm, called DOCKS (design of compact k-mer sets), works in two phases: (i) finding a minimum-size k-mer set that hits every infinite sequence; (ii) greedily adding k-mers such that together they hit all remaining L-long sequences. We show that DOCKS works well in practice and produces a set of k-mers that is much smaller than a random choice of k-mers. We present results for various values of k and sequence lengths L and by applying them to two bacterial genomes show that universal hitting k-mers improve on minimizers. The software and exemplary sets are freely available at

Original languageEnglish
Title of host publicationAlgorithms in Bioinformatics - 16th International Workshop, WABI 2016, Proceedings
EditorsMartin Frith, Christian Nørgaard Storm Pedersen
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319436807
StatePublished - 2016
Externally publishedYes
Event16th International Workshop on Algorithms in Bioinformatics, WABI 2016 - Aarhus, Denmark
Duration: 22 Aug 201624 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9838 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Workshop on Algorithms in Bioinformatics, WABI 2016

Bibliographical note

Funding Information:
R.S. was supported in part by the Israel Science Foundation as part of the ISF-NSFC joint program 2015–2018. D.P. was supported in part by a Ph.D. fellowship from the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University. This research is funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4554 to C.K., by the US National Science Foundation (CCF-1256087, CCF-1319998) and by the US National Institutes of Health (R01HG007104). C.K. received support as an Alfred P. Sloan Research Fellow. Part of this work was done while Y.O., R.S. and C.K. were visiting the Simons Institute for the Theory of Computing.

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


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