Fig. 7: Impact of resolution, sequence length, ensembling, distillation and multimodal training on AlphaGenome performance.

Ablation studies evaluating key model design choices across various performance metrics (y axis). For all panels, lines represent the mean over replicate training runs with different random seeds (n = 4 unless otherwise stated), and shaded contours denote the uncertainty interval (two standard deviations). a, Impact of target resolution. Performance comparison across models trained to predict targets (DNA accessibility, gene expression and splicing) at varying resolutions (x axis; 1–128 bp). b, Impact of sequence length during training and inference. Blue dots represent a single set of models trained with 1-Mb input, evaluated using varying input sequence lengths (x axis). Purple crosses represent models trained at the sequence length indicated on the x axis but evaluated at a fixed 1-Mb input length. Green triangles represent models trained and evaluated using the same matched sequence length (x axis). c, Impact of the number of sub-models in ensembling and distillation. Performance comparison for mean ensembles of pretrained models (blue dots/contours; x axis indicates ensemble size) versus single models produced by distillation using 1, 4 or 64 teacher models (orange crosses/contours; x axis indicates number of teachers). d, Impact of multimodal learning. Performance comparison evaluating models trained only on specific modality groups (blue dots; n = 8 seeds per group, highlighted in green if the modality matches the evaluation metric) against the full multimodal model (black dashed line; n = 4 seeds average). During training for these models, we ensured that only the target modality group’s prediction heads contributed updates to the shared representations, allowing assessment of that modality group’s contribution to overall model performance. Groups shown (x axis) include models trained using gradients only from accessibility (ATAC, DNase and contact maps), expression (RNA-seq, CAGE and PRO-cap), splicing (sites, usage and junctions) or histone ChIP-seq.