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Disk Compression of k-mer Sets

Authors Amatur Rahman, Rayan Chikhi, Paul Medvedev

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Author Details

Amatur Rahman
  • Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA
Rayan Chikhi
  • Department of Computational Biology, C3BI USR 3756 CNRS, Institut Pasteur, Paris, France
Paul Medvedev
  • Pennsylvania State University, University Park, PA, USA

Funding

PM and AR were supported by NSF awards 1453527 and 1439057.
  • Rahman, Amatur: AR is supported by NIH Computation, Bioinformatics, and Statistics training program.
  • Chikhi, Rayan: INCEPTION project (PIA/ANR-16-CONV-0005).

Cite AsGet BibTex

Amatur Rahman, Rayan Chikhi, and Paul Medvedev. Disk Compression of k-mer Sets. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 16:1-16:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.WABI.2020.16

Abstract

K-mer based methods have become prevalent in many areas of bioinformatics. In applications such as database search, they often work with large multi-terabyte-sized datasets. Storing such large datasets is a detriment to tool developers, tool users, and reproducibility efforts. General purpose compressors like gzip, or those designed for read data, are sub-optimal because they do not take into account the specific redundancy pattern in k-mer sets. In our earlier work (Rahman and Medvedev, RECOMB 2020), we presented an algorithm UST-Compress that uses a spectrum-preserving string set representation to compress a set of k-mers to disk. In this paper, we present two improved methods for disk compression of k-mer sets, called ESS-Compress and ESS-Tip-Compress. They use a more relaxed notion of string set representation to further remove redundancy from the representation of UST-Compress. We explore their behavior both theoretically and on real data. We show that they improve the compression sizes achieved by UST-Compress by up to 27 percent, across a breadth of datasets. We also derive lower bounds on how well this type of compression strategy can hope to do.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • de Bruijn graphs
  • compression
  • k-mer sets
  • spectrum-preserving string sets

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Supplementary Materials

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