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LCPan: efficient variation graph construction using Locally Consistent Parsing.

Akmuhammet Ashyralyyev, Zülal Bingöl, Begüm Filiz Öz

    Arxiv
    |December 3, 2025
    PubMed
    Summary
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    Locally Consistent Parsing (LCP) efficiently processes genomic data by partitioning strings into consistent substrings. This iterative method, Lcptools, refines cores for compact representation and faster analysis, outperforming sketching techniques.

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomic Data Analysis

    Background:

    • Genomic data is rapidly increasing, necessitating efficient string processing.
    • Locally Consistent Parsing (LCP) offers a method for partitioning genome strings into consistent substrings called 'cores'.

    Purpose of the Study:

    • To present the first iterative implementation of LCP using Lcptools.
    • To demonstrate LCP's effectiveness in identifying cores with minimal collisions.
    • To introduce LCPan for efficient variation graph construction.

    Main Methods:

    • Implemented an iterative version of Locally Consistent Parsing (LCP).
    • Utilized Lcptools for core identification and refinement.
    • Developed LCPan, a variation graph constructor.

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    Main Results:

    • The number of cores decreases exponentially with iterations (O(n/c^i), c ~ 2.34).
    • Average core length and distance between cores increase polynomially (O(c^i)).
    • LCPan constructs variation graphs >10x faster and uses >13x less memory than vg.

    Conclusions:

    • Iterative LCP provides a compact and informative representation of genomic data.
    • Lcptools enables efficient and consistent core identification with minimal collisions.
    • LCPan significantly improves computational and memory efficiency for variation graph construction.