README.md

---
pretty_name: Evolutionary MSA Data
size_categories:
  - 100K<n<1M
task_categories:
  - feature-extraction
  - other
tags:
  - biology
  - protein
  - msa
  - evolutionary
  - sequence-alignment
  - mmseqs2
  - archive
configs:
  - config_name: files
    default: true
    data_files:
      - split: train
        path: metadata.csv
  - config_name: shards
    data_files:
      - split: train
        path: shards.csv
  - config_name: parts
    data_files:
      - split: train
        path: parts.csv
---

# Evolutionary MSA Data

This repository contains precomputed evolutionary sequence-alignment data in an archive format that is practical to host and download from the Hub. The original file paths are preserved inside the tar shard, while `metadata.csv` gives a searchable index of every file.

The dataset is meant for workflows that need ready-to-use MSA/cache files without rebuilding them from sequence databases.

## Contents

| Component | Files | Size |
|---|---:|---:|
| `msa_cache/` | 134,898 | 19.37 GiB |
| `mmseqs30/` | 4 | 82.32 MiB |
| root files | 4 | 41.99 MiB |

File types:

| Type | Files | Size |
|---|---:|---:|
| `.npz` | 134,898 | 19.37 GiB |
| `.fasta` | 3 | 80.71 MiB |
| `.parquet` | 1 | 34.33 MiB |
| `.json` | 1 | 7.65 MiB |
| `.tsv` | 1 | 1.61 MiB |
| `.sh` | 1 | 806 B |
| `.md` | 1 | 543 B |

Packaging:

| Item | Value |
|---|---:|
| Indexed files | 134,906 |
| Original payload | 19.49 GiB |
| Tar shards | 1 |
| Split large-file parts | 0 |
| Archive size | 19.72 GiB |
| Metadata generated | 2026-05-25T06:39:53Z |

## Layout

```text
README.md
_MANIFEST.json
metadata.csv
shards.csv
parts.csv
shards/
  shard-00000.tar
```

Most users only need `metadata.csv` and `shards/shard-00000.tar`. The metadata table is configured as the default Dataset Viewer table.

## Metadata

`metadata.csv` has one row per original file.

| Column | Meaning |
|---|---|
| `path` | Original relative path. |
| `storage_type` | `tar` for files stored inside a tar shard; `parts` for oversized files split into byte parts. |
| `shard_path` | Tar shard containing the file. |
| `member_path` | Path of the file inside the tar shard. |
| `parts_count` | Number of split parts, if any. |
| `part_paths` | Semicolon-separated part paths, if any. |
| `top_level`, `directory`, `filename`, `extension` | Path fields for filtering. |
| `size_bytes`, `size_human`, `modified_utc` | Size and timestamp captured during packaging. |

`shards.csv` lists archive shards. `parts.csv` is present for consistency with the archive format; this upload does not currently require split parts.

## Python API Examples

Install recent Hugging Face clients in your environment:

```python
# pip install -U huggingface_hub datasets
```

Set the repo id once:

```python
repo_id = "LiteFold/Evolutionary"
```

### Browse Files

```python
from datasets import load_dataset

files = load_dataset(repo_id, "files", split="train")
print(files)
print(files[0])
```

For streaming access to the index:

```python
from datasets import load_dataset

files = load_dataset(repo_id, "files", split="train", streaming=True)
for row in files:
    if row["extension"] == ".npz":
        print(row["path"], row["size_human"], row["shard_path"])
        break
```

### Extract One MSA Cache File

This downloads the shard containing the selected file, then extracts only that member.

```python
from pathlib import Path
import tarfile

from datasets import load_dataset
from huggingface_hub import hf_hub_download

repo_id = "LiteFold/Evolutionary"
out_dir = Path("./evolutionary")

files = load_dataset(repo_id, "files", split="train", streaming=True)
row = next(item for item in files if item["extension"] == ".npz")

shard = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=row["shard_path"],
)

with tarfile.open(shard) as archive:
    archive.extract(row["member_path"], path=out_dir)

print(out_dir / row["path"])
```

### Restore The Full Tree

This downloads the repository snapshot and extracts all tar shards into a local directory.

```python
from pathlib import Path
import csv
import tarfile

from huggingface_hub import snapshot_download

repo_id = "LiteFold/Evolutionary"
snapshot = Path(snapshot_download(repo_id=repo_id, repo_type="dataset"))
out_dir = Path("./evolutionary")
out_dir.mkdir(parents=True, exist_ok=True)

for shard in sorted((snapshot / "shards").glob("*.tar")):
    with tarfile.open(shard) as archive:
        archive.extractall(out_dir)

with (snapshot / "metadata.csv").open(newline="") as handle:
    for row in csv.DictReader(handle):
        if row["storage_type"] != "parts":
            continue
        target = out_dir / row["path"]
        target.parent.mkdir(parents=True, exist_ok=True)
        with target.open("wb") as dst:
            for part_path in row["part_paths"].split(";"):
                with (snapshot / part_path).open("rb") as src:
                    while chunk := src.read(8 * 1024 * 1024):
                        dst.write(chunk)

print(out_dir)
```

## Notes

- The archive is uncompressed so individual files can be extracted directly from the tar shard.
- The file paths are kept as they appeared in the source data directory.
- Use `metadata.csv` as the source of truth for locating files inside shards.