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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Genome graphs provide a compact representation of genetic variability, surpassing traditional references.
    • Sequence-to-graph alignment is crucial for graph-based genome analysis but computationally intensive.
    • Standardized comparison of sequence-to-graph alignment tools is lacking due to complexity and novelty.

    Purpose of the Study:

    • To propose a methodology for comprehensive comparison of sequence-to-graph alignment tools.
    • To introduce the first open-source benchmark suite for quantitative analysis of these aligners.
    • To provide guidelines for improving sequence-to-graph alignment algorithms.

    Main Methods:

    • Defined Key Performance Indicators (KPIs) for qualitative analysis (usability, accuracy, performance).
    • Developed an open-source benchmark suite for quantitative assessment of multiple aligners.
    • Tested the methodology on state-of-the-art sequence-to-graph alignment tools.

    Main Results:

    • The proposed methodology facilitates structured comparison of alignment tools.
    • The benchmark suite provides valuable insights into aligner performance.
    • Demonstrated the effectiveness of the approach on current state-of-the-art tools.

    Conclusions:

    • The developed methodology and benchmark suite enable effective evaluation of sequence-to-graph aligners.
    • This work addresses the need for standardization in comparing genome graph alignment tools.
    • Guidelines are provided to advance the field of graph-based genome analysis.