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Faster Maximal Exact Matches with Lazy LCP Evaluation.

Adrián Goga1, Lore Depuydt2, Nathaniel K Brown3

  • 1Dept. of Comp. Sci., Comenius University, Bratislava, Slovakia.

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This study introduces a lazy evaluation method for Longest Common Extension (LCE) queries in the MONI algorithm. This optimization bounds processing time by the number of maximal exact matches (MEMs) found in repetitive genomes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The MONI algorithm uses Burrows-Wheeler Transform (BWT) for efficient pattern matching in repetitive genomic databases.
  • Current MONI performance is bottlenecked by time-intensive Longest Common Extension (LCE) queries.

Purpose of the Study:

  • To optimize MONI's performance by improving the efficiency of LCE queries.
  • To bound the total processing time of MONI based on the number of maximal exact matches (MEMs).

Main Methods:

  • Implementation of a variant of LCE queries using lazy evaluation.
  • Analysis of the time complexity with respect to LF-steps and LCE queries.

Main Results:

  • The proposed lazy LCE evaluation bounds MONI's total processing time by the number of MEMs.
  • Logarithmic latency is maintained during pattern processing.

Conclusions:

  • Lazy evaluation of LCE queries offers a significant performance improvement for MONI.
  • This approach enhances the efficiency of analyzing DNA reads against large, repetitive genomic datasets.