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This study introduces a novel computational framework and the Prokrustean graph to analyze k-mer-based objects across all k-mer sizes efficiently. Our method offers a scalable solution for bioinformatics tasks, independent of k-mer size range.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • K-mer-based methods are widely used in bioinformatics but understanding k-mer size influence is challenging.
  • Optimal k-mer selection is often arbitrary and computationally complex, obscuring insights in tasks like genome analysis.
  • Existing k-mer-based objects (e.g., de Bruijn graphs) offer limited dynamic views across k-mer sizes.

Purpose of the Study:

  • To develop a generalized computational framework for analyzing k-mer-based objects across all k-mer sizes.
  • To introduce the Prokrustean graph, a novel substring index, for efficient computation of k-mer object properties.
  • To provide a scalable and computationally efficient solution independent of the range of k-mer sizes.

Main Methods:

  • Developed a computational framework utilizing a novel substring index, the Prokrustean graph.
  • Derived a space-efficient algorithm to extract the Prokrustean graph from the Burrows-Wheeler Transform.
  • Demonstrated computation of k-mer-based object quantities for all k-mer sizes with complexity dependent on maximal repeats.

Main Results:

  • The Prokrustean graph framework enables computation of k-mer object quantities for all k-mer sizes efficiently.
  • Computational complexity is solely dependent on the number of maximal repeats, not the k-mer size range.
  • Successfully implemented four applications critical for pangenomics and metagenomics, showcasing practical utility.

Conclusions:

  • The Prokrustean graph provides a unified and efficient approach to analyzing k-mer-based objects across varying k-mer sizes.
  • Modern substring indices face limitations in exploring diverse k-mer sizes due to substring grouping difficulties.
  • This framework significantly advances computational efficiency and scalability for pangenomics and metagenomics research.