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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Probabilistic models, such as pair hidden Markov models (pair-HMMs), are crucial for accurate biological sequence alignment.
    • Pair-HMMs excel in detecting sequence homology but have limitations impacting performance in certain alignment tasks.

    Purpose of the Study:

    • To introduce a novel message-passing scheme to overcome the limitations of traditional pair-HMMs in biological sequence alignment.
    • To develop a more accurate and efficient method for comparing and aligning biological sequences.

    Main Methods:

    • A message-passing approach is employed, facilitating communication between potentially aligned neighboring symbol pairs.
    • The scheme generates probabilistic symbol alignment confidence scores to predict optimal alignments.

    Main Results:

    • The proposed message-passing scheme demonstrates superior alignment accuracy compared to pair-HMMs on protein alignment benchmarks.
    • The new method achieves higher computational efficiency than traditional pair-HMM approaches.

    Conclusions:

    • The message-passing scheme offers a robust and numerically stable alternative for sequence alignment.
    • The approach is highly amenable to massive parallelization, enhancing its practical applicability in large-scale analyses.