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Effloc: An Efficient Locating Algorithm for Mass-Occurrence Biological Patterns with FM-Index.

Li-Lu Guo1

  • 1Department of Computer Science, Xidian University, Xi'an, Shaanxi, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 2, 2025
PubMed
Summary
This summary is machine-generated.

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Effloc is a new algorithm that speeds up biological pattern locating in large genomes, especially for frequently occurring patterns. It reduces computational steps, making genomic analysis more efficient.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Analysis

Background:

  • Pattern locating is essential for biological sequence analysis.
  • Existing full-text index methods struggle with mass-occurrence patterns in ultra-long genomes due to slow locating times.
  • There is a need for efficient algorithms to handle frequent patterns in large genomic datasets.

Purpose of the Study:

  • To propose an efficient algorithm, Effloc, for locating mass-occurrence biological patterns in genomic sequences.
  • To optimize the locating process by reducing computationally expensive operations.
  • To improve the speed and efficiency of pattern searching in large-scale genomic data.

Main Methods:

  • Developed Effloc based on two key optimization techniques.
Keywords:
FM-Indexbiological patterngroupinglocating algorithmsampling sharing

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  • Grouped and calculated rankings with the same Burrows-Wheeler Transform character to reduce last-to-first column (LF) mapping operations.
  • Designed a specific structure to record jump status, avoiding redundant LF mapping operations for adjacent patterns.

    • Main Results:

    • Effloc significantly reduces the number of time-consuming LF mapping operations during mass-occurrence pattern locating.
    • Ablation experiments confirmed the algorithm's effectiveness.
    • Effloc demonstrated faster locating speeds compared to five state-of-the-art competing algorithms.

    Conclusions:

    • Effloc provides an efficient solution for locating mass-occurrence biological patterns in genomic sequences.
    • The algorithm offers a significant improvement in speed and reduces computational overhead.
    • The proposed method enhances the efficiency of biological sequence analysis for large datasets.