Rethinking Genomic Modeling Through Optical Character Recognition
Hongxin Xiang, Pengsen Ma, Yunkang Cao, Di Yu, Haowen Chen, Xinyu Yang, Xiangxiang Zeng
ICML 2026
cs.CV, cs.AI, cs.CL, cs.LG
2026-02-02
OpticalDNA treats DNA as OCR documents. On 450kb eQTL it hits 0.852 AUROC with a 256k probe, beating Enformer (0.681) with nearly 20× fewer tokens.
Functional DNA is sparse. Regulatory variants, splice sites, and other useful intervals sit inside long stretches of low-information sequence. Genomic foundation models inherited the LLM habit of reading A/T/C/G as a one-dimensional token stream. Most of the compute then goes to background, and long-range jumps never become first-class interval operations.
Compression should be the point of long-context genomics, yet high-fidelity compression needs to know what to keep. HyenaDNA, Caduceus, and Nucleotide Transformer still charge a similar cost per base or k-mer. At 450kb eQTL inputs, NT-v2-500M and GENERator-1.2B cannot take the sequence natively in this setup, so they are chunked and linearly probed. Tasks biologists actually run (locate a variant, retrieve a subsequence, fill a gap) stay implicit in positional encodings.
OpticalDNA, from Hunan University and Yuelushan Laboratory, recasts genomic modeling as OCR-style document understanding. DNA is rasterized in a monospace layout onto 640×640 pages (font size 14, line spacing 1.6, about 1,800 bases per page) and paginated when it overflows. Only A/C/G/T/N appear on the canvas. Every nucleotide keeps a pixel box, so sequence intervals and image regions map both ways. The claim is computational, not biological: DNA is still a 1D polymer, the 2D page is a way to skip instead of scan.
The visual encoder is DeepSeek-OCR's frozen SAM–Conv–CLIP-L front-end: 16×16 patches, a 16× convolutional token downsample, a projector, then a one-layer 20-head fusion that collapses all pages into 100 document tokens. The decoder is DeepSeek-3B MoE with about 570M activated parameters, tuned with LoRA. After pretraining the decoder is dropped. Downstream heads are a 256k linear probe or a 1.3M–2.3M MLP.
Six prompt families train the OCR loop: full-page transcription, grounded reading, ROI reading, masked span completion, subsequence localization, and chromosome classification. Human pretraining samples 2,048-bp windows from HG38 on 8×H100 for 227,600 then 190,000 steps (about 11 days). A rice model is trained for 150,000 steps on the Nipponbare T2T genome.
The main human benchmark is DNALONGBENCH eQTL prediction on 450kb inputs across nine GTEx tissues (AUROC).
| Method | Trainable params | Mean AUROC |
| Enformer | 252M | 0.681 |
| HyenaDNA | 1.6M | 0.514 |
| Caduceus-Ph | 7.7M | 0.750 |
| NT-v2-500M (chunked) | 1.03K | 0.772 |
| GENERator-1.2B (chunked) | 10.24K | 0.782 |
| JanusDNA | 7.7M | 0.791 |
| JanusDNA MLP | 7.7M | 0.840 |
| OpticalDNA linear probe | 256K | 0.852 |
| OpticalDNA MLP | 1.3M–2.3M | 0.867 |
Raising render resolution from 512 to 1280 grows visual tokens per page from 64 to 400 and the compression ratio from 19.0 to 21.2; mean AUROC stays in 0.849–0.852. On whole blood the MLP variant reaches 0.927 versus JanusDNA MLP at 0.821; on thyroid, 0.876 versus 0.793.
A 409M rice encoder is trained separately. On RiceSubBench splice-site classification, japonica accuracy/AUROC is 0.590/0.739 and far-OOD O. glaberrima is 0.599/0.731, ahead of Evo-2 7B (0.486/0.700 and 0.489/0.705) and LucaOne 1.8B (0.510/0.703 and 0.526/0.736). On whole-genome RiceWGPB (400M bases), thousand-grain weight and leaf-rolling RMSE are 2.952 and 9.531 in 12.3 minutes, against Evo-2 at 3.056/9.617 in 5h40m.
Against an unmodified DeepSeek-OCR backbone, mean eQTL AUROC moves from 0.823 to 0.867. Full-page transcription exact match is 79.6% on HG38 and 74.9% on rice, versus about 1.2% for DeepSeek-OCR. With a matched 10,000-token budget and the same Transformer, the 2D document representation scores 0.626 AUROC against 0.558 for 1D sequence tokens. A lightweight TinyNet-E 2D CNN hits 0.786; the strongest 1D CNN variant in that sweep only reaches 0.649.
For long-range regulation and whole-genome traits, the usual tax is paying full attention on background bases. OpticalDNA is a reproducible detour: print the sequence without loss, compress it visually by about 20×, and adapt with a 256k probe. On 450kb human eQTL that probe already clears a line of sequence foundation models.
This is a change of representation, not a new biological prior. Downstream runs do not even keep the OCR decoder; the working object is the visual encoder plus a small head. Teams already predicting on ultra-long DNA who can tolerate a rendering step have something to try. Short-sequence classification and base-level generation still fit sequence models better.
Chromosome classification on 2,048-bp windows is effectively unsolved: 0.062 accuracy on HG38 and 0.104 on rice. The authors say local motifs recur across chromosomes, so short context is not distinctive. Transcription exact match falls to 42.3% near single-page capacity (1,792–2,048 bp). The abstract's "985× fewer activated parameters" compares the 256k probe with Enformer's 252M; the frozen visual encoder is still about 409M. Appendix FLOPs per base are 177.76 MFLOPs for OpticalDNA, above JanusDNA at 17.95 and Caduceus at 27.27, and fine-tuning throughput is 0.12 M bases/s versus Caduceus at 0.52. The savings are in effective tokens and wall-clock against 7B sequence models, not a universal FLOP win.
RiceWGPB has 130 and 102 accessions. Thousand-grain-weight RMSE of 2.952 versus 3.056 is a thin gap on a tiny set. NT-v2 and GENERator are chunked because they cannot take 450kb, so that comparison is asymmetric. Fixed-window layouts drop adipose-subcutaneous AUROC from 0.813 to 0.758–0.774; the representation is sensitive to whether local sequence stays visually continuous. Pretraining is not cheap: about 11 days on 8×H100 for the human model.