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Compression of Multiple k-Mer Sets by Iterative SPSS Decomposition

Authors Kazushi Kitaya, Tetsuo Shibuya

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Author Details

Kazushi Kitaya
  • Tokyo, Japan
Tetsuo Shibuya
  • Human Genome Center, Institute of Medical Science, The University of Tokyo, Japan

Funding

This work was supported by JSPS KAKENHI Grant 17H01693, 20K21827, and JST CREST Grant JPMJCR1402JST. The super-computing resource was provided by Human Genome Center, the Institute of Medical Science, the University of Tokyo.
  • Shibuya, Tetsuo: https://orcid.org/0000-0003-1514-5766

Cite As Get BibTex

Kazushi Kitaya and Tetsuo Shibuya. Compression of Multiple k-Mer Sets by Iterative SPSS Decomposition. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.WABI.2021.12

Abstract

A set of k-mers is used in many bioinformatics tasks, and much work has been done on methods to efficiently represent or compress a single set of k-mers. However, methods for compressing multiple k-mer sets have been less studied in spite of their obvious benefits for researchers and genome-related database maintainers. This paper proposes an algorithm to compress multiple k-mer sets, which works by iteratively splitting SPSS (spectrum-preserving string sets). In experiments with 3292 k-mer sets constructed from E. coli whole-genome sequencing data and 2555 k-mer sets constructed from human RNA-Seq data, the proposed algorithm could reduce the compressed file sizes by 34.7% and 13.2% respectively compared to one of the state-of-the-art colored de Bruijn graph representations. Also, our method used less memory than the colored de Bruijn graph method. This paper also introduces various methods to make the compression algorithm efficient in terms of time and memory, one of which is a parallelizable small-weight SPSS construction algorithm.

Subject Classification

ACM Subject Classification
  • Applied computing → Molecular sequence analysis
Keywords
  • sequencing data
  • k-mer
  • de Bruijn graph
  • compression
  • colored de Bruijn graph

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References

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