README.md

---
pretty_name: OpenGenome2-small
license: apache-2.0
task_categories:
- text-generation
tags:
- biology
- dna
- genomics
- metagenomics
- language-modeling
- evo2
- opengenome2
size_categories:
- 1M<n<10M
viewer: false
---

# OpenGenome2-small (og2_small)

**An 11.8B-token, per-base-uniform sample of [OpenGenome2](https://huggingface.co/datasets/arcinstitute/opengenome2)**,
the ~8.8T-token DNA corpus used to train Evo 2, with the same mix of eukaryotic genomes, prokaryotic genomes,
metagenomes, transcripts, viruses and regulatory sequence, plus a validation set of species never seen in training.

| | |
|---|---|
| **Train** | 11.78B bases in 1.54M windows (≤ 16,384 bp), ~39.3k species, 226 phyla |
| **Held-out species** | 124M bases, 367 species absent from train (1% of species) |
| **Evo 2 valid** | 99M bases from OpenGenome2's own validation split |
| **Baseline** | 1.940 bits/base on held-out species (order-7 Markov model) |
| **Source** | `arcinstitute/opengenome2`, revision [`84d2a7e`](https://huggingface.co/datasets/arcinstitute/opengenome2/tree/84d2a7e690c8bb395d1f4868822dfa031ea561c6), sampling rate 0.131% |
| **License** | Apache 2.0 (as OpenGenome2) |

## Quick start

```bash
hf download AutomatedScientist/og2_small --repo-type dataset --local-dir og2_small
cd og2_small && pip install -r scripts/requirements.txt torch
```

```python
import sys; sys.path.insert(0, "scripts")
import torch.nn.functional as F
from og2dataloader import RandomCrops, EvalTiles, make_loader

train = make_loader(RandomCrops(".", "train", ctx=4096, seed=0, rc_prob=0.5), batch_size=16, num_workers=4)
heldout = make_loader(EvalTiles(".", "heldout", ctx=4096), batch_size=16)

batch = next(iter(train))  # input_ids, labels, loss_mask, lowercase: each [16, 4096]
loss = F.cross_entropy(logits.flatten(0, 1), batch["labels"].flatten(), ignore_index=-100)
```

The data are numpy arrays with JSONL indexes (one byte per base), read with the scripts in `scripts/`; there is no
Parquet copy, so `datasets.load_dataset` and the dataset viewer do not apply.

## Splits

| split | bases | windows | species | what it measures |
|---|---|---|---|---|
| `train` | 11.78B | 1,539,679 | ~39.3k | training |
| `heldout` | 124M | 15,940 | 367 | generalization to unseen species |
| `valid` | 99M | 7,811 | 59 | Evo 2's validation split (comparability) |

- **`heldout`** is drawn from the same source shards as `train`: every record of ~1% of species (chosen by a hash of
  the species name) and ~1% of untagged records, sampled at the same rate. It shares **no species** with `train` and
  has the same subset mix, so it is the split to report.
- **`valid`** samples OpenGenome2's own `valid` shards. Evo 2 held those out by genomic region, not by species: 31 of
  its 59 species (80% of its tagged bases) also occur in `train`, and 91% of it is eukaryotic genomes.
- Species are distinct `S__` taxonomy tags. Only eukaryotic genomes and GTDB carry tags; metagenomes, transcripts and
  viruses add diversity that is not counted as species.

## Composition

Every base of OpenGenome2's training shards had the same chance (0.131%) of being kept, so subset shares match the
full corpus:

| subset | train bases | share | share in OpenGenome2 | species | heldout bases |
|---|---|---|---|---|---|
| ncbi_eukaryotic_genomes | 8.69B | 73.8% | 74.5% | 12,058 | 93.8M |
| metagenomes | 1.11B | 9.42% | 9.15% | – | 11.0M |
| eukaryotic_genic_windows | 545M | 4.62% | 4.50% | – | 5.8M |
| gtdb_v220_stitched | 463M | 3.93% | 3.83% | 27,259 | 4.1M |
| gtdb_v220_imgpr | 461M | 3.91% | 3.83% | – | 4.5M |
| mrna_splice_promoter | 257M | 2.18% | 2.11% | – | 2.7M |
| mrna | 151M | 1.28% | 1.24% | – | 1.5M |
| imgvr_untagged | 46.9M | 0.40% | 0.39% | – | 0.5M |
| ncrna | 42.7M | 0.36% | 0.35% | – | 0.4M |
| imgpr | 8.0M | 0.07% | 0.06% | – | 0.1M |
| organelle | 7.9M | 0.07% | 0.06% | – | 0.1M |
| promoters | 0.3M | <0.01% | <0.01% | – | <0.1M |

"–" = the subset carries no species tags. Eukaryotic genomes are slightly under-represented (73.8% vs 74.5%); see
[Sampling note](#sampling-note).

**Domains** (share of train bases): Eukaryota 73.8% · untagged 22.3% · Bacteria 3.8% · Archaea 0.12%.

**Largest phyla:**

| phylum | share | | phylum | share |
|---|---|---|---|---|
| Chordata | 33.0% | | Bacteroidota | 0.54% |
| Streptophyta | 18.8% | | Actinomycetota | 0.53% |
| Arthropoda | 15.6% | | Cnidaria | 0.50% |
| Mollusca | 2.48% | | Basidiomycota | 0.47% |
| Pseudomonadota | 1.11% | | Annelida | 0.43% |
| Ascomycota | 0.99% | | Echinodermata | 0.41% |

## Statistics

| split | A | C | G | T | N | GC | soft-masked | CpG o/e |
|---|---|---|---|---|---|---|---|---|
| `train` | 28.9% | 21.1% | 21.1% | 28.9% | 0.005% | 42.2% | 32.5% | 0.75 |
| `heldout` | 28.8% | 21.2% | 21.2% | 28.8% | 0.006% | 42.5% | 34.8% | 0.76 |
| `valid` | 27.8% | 22.2% | 22.3% | 27.8% | 0.001% | 44.5% | 25.3% | 0.94 |

| group (train) | bases | GC | soft-masked | CpG o/e |
|---|---|---|---|---|
| Eukaryota | 8.69B | 39.5% | 42.1% | 0.56 |
| untagged (metagenomes, transcripts, viruses, ...) | 2.63B | 49.1% | 6.6% | 1.02 |
| Bacteria | 449M | 54.2% | – | 1.17 |
| Archaea | 13.9M | 47.3% | – | 1.09 |

- The corpus is AT-rich (GC 42%) because eukaryotic genomes dominate; prokaryotes and metagenomes sit at 50–57% GC.
- CpG is depleted in eukaryotes (observed/expected 0.56, methylation-driven) but not in bacteria (1.17).
- Soft-masking (lowercase = repeat-masked) exists only in eukaryotic data: 42% of eukaryotic bases.
- Strand symmetry holds (Chargaff's second rule): 4-mer and reverse-complement frequencies differ by 0.02% on
  average in `train`.
- Most frequent 8-mers: `AAAAAAAA`/`TTTTTTTT` (0.075% each), then `ATATATAT`/`TATATATA` and `(CA)n`/`(TG)n` repeats.

Per-subset and per-domain statistics and the full 8-mer counts are in `stats/`.

## Baselines

Order-k Markov models fitted on `train` (8-mer counts, add-½ smoothing), in bits per base:

| split | k=0 | k=1 | k=3 | k=5 | k=7 |
|---|---|---|---|---|---|
| `heldout` | 1.984 | 1.973 | 1.961 | 1.952 | **1.940** |
| `valid` | 1.993 | 1.988 | 1.976 | 1.969 | **1.959** |
| `train` (in sample) | 1.982 | 1.971 | 1.960 | 1.953 | 1.942 |

Per subset on `heldout`, best order (k=7 unless noted):

| subset | bits/base | | subset | bits/base |
|---|---|---|---|---|
| ncbi_eukaryotic_genomes | 1.930 | | mrna_splice_promoter | 1.950 |
| eukaryotic_genic_windows | 1.931 | | mrna | 1.967 |
| gtdb_v220_stitched | 1.982 | | ncrna | 1.973 |
| metagenomes | 1.984 | | imgvr_untagged | 1.980 |
| gtdb_v220_imgpr | 1.986 | | organelle | 1.963 (k=1) |

- Scored positions are those whose target and 7 preceding bases are all A/C/G/T, identical for every order.
- One model is fitted on the pooled (eukaryote-dominated) composition. GC-rich subsets therefore score near 2 bits,
  and above 2 at low orders.
- Compare a trained model's masked bits/base on `heldout` with 1.940.

## Format

```
README.md
plan_train_eukaryotic.json, plan_train_rest.json, plan_valid.json   sampling parameters of each pass
train/<subset>/<shard>.npy     kept windows of one OpenGenome2 shard, concatenated (uint8)
train/<subset>/<shard>.jsonl   one line per window: offset, len, u, tag, rec
train/<subset>/<shard>.done    per-shard statistics
train/selected.json            window selection;  train/manifest.json  composition
heldout/, valid/               same layout
stats/                         statistics (<split>.json), 8-mer counts (<split>_kmer8.npz), baselines (baseline.json)
scripts/                       build, check and load (below)
```

Sizes: `train` 12 GB, `heldout` 129 MB, `valid` 97 MB; 5.6k files.

- **Encoding:** one byte per base. Bits 0–2 hold the base (A=0, C=1, G=2, T=3, N or other IUPAC=4), and bit 7 (0x80)
  marks a soft-masked (lowercase) base. `codes & 7` gives the base and `codes >> 7` the mask.
- **Windows** never cross a record, contig or taxonomy-tag boundary.
- **`tag`** is the window's full lineage, e.g.
  `D__EUKARYOTA;P__CHORDATA;C__MAMMALIA;O__PRIMATES;F__HOMINIDAE;G__HOMO;S__HOMO SAPIENS`, or empty for untagged
  subsets.

### Scripts

| script | purpose |
|---|---|
| `og2dataloader.py` | PyTorch datasets: `RandomCrops` (training) and `EvalTiles` (evaluation, every target once) |
| `og2load.py` | numpy reader: iterate windows, filter by subset or taxon, random crops |
| `og2subset.py` | the streaming sampler (`sample`, `finalize`, `report`) |
| `build.sh` | exact build recipe |
| `og2stats.py` | composition, GC, soft-masking, CpG, k-mer statistics |
| `og2baseline.py` | Markov baselines |
| `check_rates.py` | realized sampling rate per subset |
| `species_overlap.py` | species shared between splits |

**Data loader.** Each sample is `ctx + 1` consecutive bases of one window.
- Fields: `input_ids` (base ids, N = 4), `labels` (the next base), `loss_mask` and `lowercase`.
- With `mask_non_acgt=True` (the default), targets that are not A/C/G/T get label −100 and are excluded from the loss.
- `RandomCrops` weights windows by their number of possible crops, so every base is equally likely to be seen. It is
  reproducible per (seed, worker) and can reverse-complement samples (`rc_prob`).
- Both datasets accept `subset=` and `taxon=` filters, e.g. `taxon=r"S__HOMO SAPIENS"` or `taxon=r"D__ARCHAEA"`.

## How it was built

1. **Streaming.** All 902 training shards of OpenGenome2 (`json/*/<subset>/*_train_*.jsonl.gz`, 2.79 TB) were
   streamed and decompressed in memory; only the sample was written.
2. **Windows.** Each record is split at taxonomy tags (`|D__…;S__…|`), contig separators (`#`) and window separators
   (`@`). These characters never enter a sequence. Each segment is tiled into 16,384-bp windows on a fixed grid.
3. **Per-base-uniform sampling.** Each window is kept when a uniform draw `u` is below the rate (0.131%), whatever its
   length, so every base has the same inclusion probability. `u` is stored: `og2subset.py finalize --target-tokens N`
   shrinks the sample uniformly.
4. **Held-out species.** A record goes to `heldout` when `blake2b(species name) / 2^64 < 0.01`; untagged records are
   hashed by record. All records of a species go the same way in every shard and subset.
5. **Determinism.** Seed 0 and a pinned source revision. Each shard's generator is seeded by (seed, shard path), each
   record's by (shard, record index), so the output does not depend on worker count or order.

### Sampling note

`ncbi_eukaryotic_genomes` was sampled in an earlier pass. That pass kept a window of length L with probability
rate × L / 16,384, which under-samples short windows. Eukaryotic records are ~20 Mb, so almost all windows are full
length, and the realized rate is 0.964 × rate. The deficit falls on segment ends next to tags and contig breaks. All
other subsets were re-sampled with the per-base rule; their realized rates are 0.995–1.005 × rate (the three smallest
subsets: 0.94–1.09, sampling noise). `build.sh` reproduces both passes exactly, and `check_rates.py` reports the
realized rates.

## Reproduce

```bash
pip install -r scripts/requirements.txt
bash scripts/build.sh          # stages: euk rest valid finalize stats baseline
```

About 3–4 hours on 14 CPU cores with ~250 MB/s of bandwidth (CPU-bound). Every stage is resumable.

## Limitations

- **Context:** windows are at most 16,384 bp, so context beyond 16 kb is not available.
- **Overlaps from the source:** proportions are OpenGenome2's raw ones, including its overlaps. GTDB genomes appear
  in two subsets (`gtdb_v220_stitched`, `gtdb_v220_imgpr`), and mRNA in two (`mrna`, `mrna_splice_promoter`). There
  is no deduplication beyond OpenGenome2's own. Evo 2 trained on a phase-dependent mixture, not on these proportions.
- **Repeats:** three quarters of the bases are eukaryotic genomes, which are rich in repeats (42% soft-masked).
- **Leakage across `heldout`:** untagged records are held out record by record, so related sequences (for example
  neighbouring metagenome chunks) can fall on both sides of the split.

## Citation

Please cite Evo 2, which introduced OpenGenome2:

```bibtex
@article{brixi2025evo2,
  title   = {Genome modeling and design across all domains of life with Evo 2},
  author  = {Brixi, Garyk and Durrant, Matthew G. and Ku, Jerome and others},
  journal = {bioRxiv},
  year    = {2025},
  doi     = {10.1101/2025.02.18.638918}
}
```

## License

Apache 2.0, the license of OpenGenome2, from which every sequence is sampled. The scripts are released under the
same license.