A randomized parallel algorithm for efficiently finding near-optimal universal hitting sets

Barış Ekim, Bonnie Berger, Yaron Orenstein

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

8 Scopus citations


As the volume of next generation sequencing data increases, an urgent need for algorithms to efficiently process the data arises. Universal hitting sets (UHS) were recently introduced as an alternative to the central idea of minimizers in sequence analysis with the hopes that they could more efficiently address common tasks such as computing hash functions for read overlap, sparse suffix arrays, and Bloom filters. A UHS is a set of k-mers that hit every sequence of length L, and can thus serve as indices to L-long sequences. Unfortunately, methods for computing small UHSs are not yet practical for real-world sequencing instances due to their serial and deterministic nature, which leads to long runtimes and high memory demands when handling typical values of k (e.g. k > 13). To address this bottleneck, we present two algorithmic innovations to significantly decrease runtime while keeping memory usage low: (i) we leverage advanced theoretical and architectural techniques to parallelize and decrease memory usage in calculating k-mer hitting numbers; and (ii) we build upon techniques from randomized Set Cover to select universal k-mers much faster. We implemented these innovations in PASHA, the first randomized parallel algorithm for generating near-optimal UHSs, which newly handles k > 13. We demonstrate empirically that PASHA produces sets only slightly larger than those of serial deterministic algorithms; moreover, the set size is provably guaranteed to be within a small constant factor of the optimal size. PASHA’s runtime and memory-usage improvements are orders of magnitude faster than the current best algorithms. We expect our newly-practical construction of UHSs to be adopted in many high-throughput sequence analysis pipelines.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
Number of pages17
ISBN (Print)9783030452568
StatePublished - 2020
Externally publishedYes
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: 10 May 202013 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

Bibliographical note

Funding Information:
Acknowledgments. This work was supported by NIH grant R01GM081871 to B.B. B.E. was supported by the MISTI MIT-Israel program at MIT and Ben-Gurion University of the Negev. We gratefully acknowledge the support of Intel Corporation for giving access to the Intel©R AI DevCloud platform used for part of this work.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • Parallelization
  • Randomization
  • Universal hitting sets


Dive into the research topics of 'A randomized parallel algorithm for efficiently finding near-optimal universal hitting sets'. Together they form a unique fingerprint.

Cite this