DeepHyb: Deep Learning-Based Inference of Hybridization with Embedded Comprehensive Multiple Sequence Alignment Features

Hybridization is a pivotal evolutionary process that shapes speciation and genetic diversity across the tree of life. Traditional methods for hybridization inference rely on site-pattern summary statistics and lack the ability to capture higher-order sequence dependencies, limiting their performance in complex scenarios. Additionally, existing tools often fail to explicitly classify non-hybrid evolutionary scenarios or assign parental–hybrid lineage identities, creating challenges for interpreting results in genomic studies. There is a critical need for interpretable, efficient methods that leverage full genomic sequence information to address these limitations. DeepHyb is a convolutional neural network (CNN)-based deep learning framework designed to infer historical hybridization events directly from multiple sequence alignment-derived site-pattern and k-mer count features. It integrates four complementary feature representations—15 summary site patterns, 256 one-base site patterns, 75 summary k-mer patterns, and 256 sequence-level k-mer patterns—to capture both short-range and higher-order sequence dependencies. In the canonical four-taxon scenario, which serves as the standard framework for evaluating phylogenetic discordance under the multispecies coalescent (one outgroup + three ingroups), DeepHyb jointly predicts hybridization status and parental–hybrid lineage combinations. On simulated data under the setting of sequence length = 100,000 and mutation rate = 0.01, our method performs with high accuracy, achieving an accuracy of 0.8773, precision of 0.5000, and F1 score of 0.6667 (vs. HyDe's 0.8501, 0.4501, and 0.6202), while maintaining a recall of 1.0. On the empirical Heliconius dataset, DeepHyb reproduces the HyDe-derived hybrid/non-hybrid labels with over 93