Benchmarking generative models for COI DNA barcoding

Abstract Cytochrome c oxidase subunit I (COI) DNA barcoding is widely used for species identification and biodiversity studies. However, COI datasets exhibit high intra-species similarity and significant inter-species imbalance, which limits sequence analyses. To address data scarcity, deep learning based generative models have been explored for sequence generation. We implemented six generative models incorporating gated recurrent unit (GRU) layers, Transformer blocks, and convolutional layers to generate species-specific COI sequences across four taxonomic groups: Cypraeidae, Drosophila, Bats, and Birds. The generated sequences were evaluated in terms of plausibility, phylogenetic consistency, and diversity. Finally, GRU-based autoregressive language model achieved the best performance. It preserved codon-level structures to real data, with GC₃ content differences (Δ) ≤ 0.004, codon bias JSD ≤ 0.013, and ORF mean length differences (Δ) < 0.05. It also reproduced genetic structures with intra-species K2P mean differences (Δ) ≤ 0.13, real–synthetic K2P mean ≤ 0.09, and barcode gap rate differences (Δ) ≤ − 0.6. Additionally, it generated sequences with minimal redundancy, indicated by JSD-kmer ≤ 0.03, Self-BLEU differences (Δ) ≤ 0.001, and AA values between 0.54 and 0.75. These results suggest that GRU-based COI sequence generation can serve as a robust simulation strategy for addressing data scarcity and imbalance in bioinformatics applications.