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DSK: k-mer counting with very low memory usage.

Guillaume Rizk1, Dominique Lavenier, Rayan Chikhi

  • 1Algorizk, 75013 Paris, France.

Bioinformatics (Oxford, England)
|January 18, 2013
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Summary
This summary is machine-generated.

A new streaming algorithm, DSK (disk streaming of k-mers), efficiently counts DNA/RNA k-mers using fixed memory and disk space. This method offers a memory, time, and disk trade-off, making it suitable for servers with limited memory.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • K-mer counting is essential for DNA/RNA sequencing analysis.
  • Existing methods demand substantial in-memory data structures.
  • Data structure size scales with the number of distinct k-mers.

Purpose of the Study:

  • Introduce DSK (disk streaming of k-mers), a novel streaming algorithm for k-mer counting.
  • Address memory limitations of current state-of-the-art k-mer counting techniques.
  • Provide an efficient alternative for k-mer analysis on resource-constrained servers.

Main Methods:

  • DSK employs a streaming approach with a fixed memory and disk footprint.
  • Partitions the multi-set of k-mers and saves them to disk.
  • Loads partitions into temporary hash tables for counting.
  • Optionally filters low-abundance k-mers.

Main Results:

  • DSK successfully counted all 27-mers in a human genome dataset using only 4.0 GB RAM and 160 GB disk space.
  • The computation time for the human genome dataset was 17.9 hours.
  • DSK demonstrates a viable memory, time, and disk trade-off.

Conclusions:

  • DSK is the first algorithm capable of counting all k-mers in large datasets with limited memory.
  • DSK can serve as a replacement for existing tools like Jellyfish on servers with restricted memory.
  • The algorithm offers efficient k-mer counting for various bioinformatics applications.