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Prefix-free parsing (PFP) data structures now support full suffix tree functionality. This enables efficient computation of genomic data primitives, outperforming existing compressed suffix trees on large datasets.

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Area of Science:

  • Bioinformatics
  • Data Structures
  • Computational Biology

Background:

  • Prefix-free parsing (PFP) preprocesses genomic data for efficient Burrows-Wheeler Transform (BWT) computation.
  • PFP generates a dictionary and parse significantly smaller than the original string, enabling faster BWT calculation for large concatenated genomes.

Purpose of the Study:

  • To augment PFP data structures to support full suffix tree functionality.
  • To achieve this within the space complexity of O(|PFP(S)|).
  • To enable efficient computation of key suffix tree primitives.

Main Methods:

  • Augmenting the PFP data structure to incorporate suffix tree capabilities.
  • Developing efficient algorithms for computing suffix array (SA), inverse suffix array (ISA), BWT, and longest common prefix (LCP) array values.
  • Implementing range minimum queries and next/previous smaller value queries on the LCP array.

Main Results:

  • Demonstrated that PFP data structures can be augmented to provide full suffix tree functionality.
  • Achieved space complexity bound of O(|PFP(S)|) for the augmented structure.
  • Experimental results show competitive performance against other compressed suffix trees on large, repetitive datasets.

Conclusions:

  • The PFP suffix tree offers a powerful and space-efficient data structure for genomic data analysis.
  • It provides efficient support for essential suffix tree operations, crucial for bioinformatics tasks.
  • This approach is particularly effective for handling very large and repetitive biological sequence data.