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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.
Proceedings. Data Compression Conference
|August 19, 2024
Summary
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.
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.
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