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Summary

We developed a new method to directly construct sparse suffix arrays (SSAs) by encoding text, reducing memory and construction time by 50-75%. This approach bypasses full suffix array construction for efficient bioinformatics data searching.

Keywords:
BioinformaticsBit-packingC programming languageLibsaisMemory-efficient text indexingPattern matchingSparse suffix array constructionString matchingText encoding

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Structures

Background:

  • Pattern matching is crucial for genomics, transcriptomics, and proteomics.
  • Suffix arrays are key indexing structures for large datasets.
  • Sparse suffix arrays (SSAs) reduce memory but typically require full suffix array construction first, causing high overhead.

Purpose of the Study:

  • To present a novel method for direct sparse suffix array construction.
  • To reduce memory usage and construction time for SSAs.
  • To improve the efficiency of pattern matching in large biological datasets.

Main Methods:

  • Developed a text encoding method to group characters and reduce input text length.
  • Extended the Libsais library to directly construct SSAs from the encoded text.
  • Implemented a subsampling strategy for higher sparseness factors.

Main Results:

  • Achieved 50-75% reduction in memory usage and construction time for SSAs (k=3, 4) on nucleotide and amino acid datasets.
  • Demonstrated similar performance gains for sparseness factors up to 8, depending on alphabet size.
  • The method is particularly effective for small alphabets common in bioinformatics.

Conclusions:

  • Introduced an efficient method for constructing sparse suffix arrays.
  • Text transformation and direct SSA construction via Libsais significantly improve execution time and memory usage.
  • The approach reduces input text and SSA length by a factor of k.