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On weighted k-mer dictionaries.

Giulio Ermanno Pibiri1,2

  • 1Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venice, Italy. giulioermanno.pibiri@unive.it.

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Summary
This summary is machine-generated.

This study introduces an enhanced weighted dictionary of k-mers for bioinformatics, compressing k-mer counts efficiently. The new method significantly improves storage and retrieval for large biological datasets.

Keywords:
CompressionGraphsHashingPath coverk-mers

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Compression

Background:

  • K-mer counting is crucial in bioinformatics but generates large datasets, causing processing bottlenecks.
  • Existing methods for storing k-mer sets and their weights are often inefficient in terms of space and speed.

Purpose of the Study:

  • To develop a compressed representation for weighted k-mer sets.
  • To extend the SSHash dictionary to compactly store k-mer weights.
  • To improve the efficiency of k-mer counting pre-processing steps in bioinformatics.

Main Methods:

  • Extension of the SSHash dictionary to incorporate weight compression.
  • Exploitation of k-mer order to encode runs of weights for enhanced compression.
  • Development of an optimal algorithm to minimize weight runs and further improve compression.

Main Results:

  • The extended SSHash dictionary achieves significantly better compression than empirical entropy of weights.
  • The proposed method demonstrates superior performance on real-world bioinformatics datasets.
  • SSHash is presented as an exact, weighted, associative, fast, and small k-mer dictionary.

Conclusions:

  • The enhanced SSHash dictionary offers an effective solution for compressing weighted k-mer sets in bioinformatics.
  • This approach alleviates processing bottlenecks associated with large k-mer count outputs.
  • SSHash provides a unique combination of features making it highly suitable for various bioinformatics applications.