Extended Data Fig. 1: Detailed model architecture of ANNEVO’s neural network component.

a, Distal Information Modeling Module. This module extracts local sequence patterns using five consecutive ConvBlocks and learns long-range dependencies through positional encoding and Transformer encoder layers. The parameters are as follows: C = 64, H = 8, D = 768. b, Joint Evolutionary Modeling Module. The module consists of eight sub-lineage networks and a relationship computation controller. The sub-lineage networks capture diverse evolutionary relationships, while the relationship computation network models the affinity between the input sequence and each EvoExpert. The parameters are as follows: C = 64, D = 768. c, Resolution Restorer Module. The Resolution Restorer Module serves as the inverse process to the ConvBlocks, designed to reconstruct the feature vector back to nucleotide resolution. Its primary purpose is to transform the 320-channel feature vector (which has been expanded from the original 64 channels by the ConvBlocks) back to a representation that aligns with the original nucleotide-level resolution, effectively inverting the channel expansion and restoring the spatial detail for gene annotation. d, Detailed Architecture of Network Blocks. The ConvBlocks progressively increase the number of channels, with the convolutional layer channels expanding from C to 5 C. After passing through five ConvBlocks, the features reach 5 C = 320 channels. Each ConvBlock compresses information from two adjacent positions, embedding information from every 32 nucleotides into the same dimension. Encoder use the classical six-layer Transformer encoder structure to minimize parameter tuning. In ANNEVO, the number of attention heads is set to H = 8, and the hidden layer dimension is D = 768. EvoExpert employs two simple linear layers designed to preserve the distinct characteristics of different sub-clades. These layers map the feature vector’s dimension from 320 to 768, and then back to 320. Relationship calculation network uses a single linear layer to map the feature vector from dimension 320 to 8, corresponding to the number of expert networks.