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Disk Compression of k-mer Sets

Authors Amatur Rahman, Rayan Chikhi, Paul Medvedev

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

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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.

Cite As Get 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

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).

Supplementary Materials

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