File size: 3,166 Bytes
f95f6ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
---
license: cc-by-4.0
task_categories:
- other
tags:
- genomics
- dna
- embeddings
- alphagenome
- hg38
- grch38
pretty_name: ALPHAGenome hg38 Embeddings
size_categories:
- 10K<n<100K
---

# ALPHAGenome hg38 Embeddings

Pre-computed **ALPHAGenome** DNA foundation model embeddings for the entire human genome (hg38 / GRCh38).

The human genome is divided into **~22,000 non-overlapping 131 KB bins**. Each bin's DNA sequence is embedded into a **3,072-dimensional latent space** using the ALPHAGenome foundation model.

## Companion project

These embeddings power the **ALPHAGenome UMAP Explorer** — an interactive browser visualization of latent relationships between genomic regions:

**→ [GitHub: DNAEmbeddings](https://github.com/lagosproject/DNAEmbeddings)**  
**→ [Live Demo](https://lagosproject.github.io/DNAEmbeddings/)**

Scripts to download this dataset and re-run the UMAP pipeline are included in that repo.

## Dataset structure

```
data/
  chr1_embeddings.npy     # float32 array, shape (N, 3072)
  chr1_metadata.csv       # columns: chrom, start, end
  chr2_embeddings.npy
  chr2_metadata.csv
  ...                     # one pair per chromosome (chr1–22, chrX, chrY)
```

Each `.npy` file and its paired `.csv` share the same row order — row `i` in the embeddings corresponds to row `i` in the metadata.

## Quick usage

```python
from huggingface_hub import hf_hub_download
import numpy as np
import pandas as pd

# Download a single chromosome
emb_path  = hf_hub_download("lagosproject/ALPHAGenome-Embeddings", "data/chr1_embeddings.npy", repo_type="dataset")
meta_path = hf_hub_download("lagosproject/ALPHAGenome-Embeddings", "data/chr1_metadata.csv",    repo_type="dataset")

embeddings = np.load(emb_path)          # (1750, 3072)
metadata   = pd.read_csv(meta_path)     # chrom | start | end
```

Or download everything at once (≈312 MB):

```python
from huggingface_hub import snapshot_download
snapshot_download("lagosproject/ALPHAGenome-Embeddings", repo_type="dataset", local_dir="res/")
```

## Statistics

| Chromosome | Windows | Embedding shape |
|---|---|---|
| chr1 | 1,750 | (1750, 3072) |
| chr2 | 1,829 | (1829, 3072) |
| chr3 | 1,509 | (1509, 3072) |
| chr4 | 1,439 | (1439, 3072) |
| chr5 | 1,379 | (1379, 3072) |
| chr6 | 1,294 | (1294, 3072) |
| chr7 | 1,206 | (1206, 3072) |
| chr8 | 1,100 | (1100, 3072) |
| chr9 | 915 | (915, 3072) |
| chr10 | 1,013 | (1013, 3072) |
| chr11 | 1,022 | (1022, 3072) |
| chr12 | 1,012 | (1012, 3072) |
| chr13 | 743 | (743, 3072) |
| chr14 | 687 | (687, 3072) |
| chr15 | 640 | (640, 3072) |
| chr16 | 619 | (619, 3072) |
| chr17 | 627 | (627, 3072) |
| chr18 | 609 | (609, 3072) |
| chr19 | 444 | (444, 3072) |
| chr20 | 482 | (482, 3072) |
| chr21 | 288 | (288, 3072) |
| chr22 | 285 | (285, 3072) |
| chrX | 1,170 | (1170, 3072) |
| chrY | 191 | (191, 3072) |
| **Total** | **22,253** | **3,072-dim** |

## Data sources

| Source | Usage |
|--------|-------|
| **ALPHAGenome** | DNA sequence embeddings (3,072-dim per 131 KB bin) |
| **UCSC hg38** | Reference genome coordinates |

## License

CC BY 4.0 — free to use for research and educational purposes with attribution.