---
pretty_name: NCBI RefSeq Protein Shard Index
license: other
tags:
- biology
- proteins
- sequences
- fasta
- ncbi
- refseq
- parquet
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: test
    path: data/test-*.parquet
---

# NCBI RefSeq Protein Shard Index

This dataset contains the original NCBI RefSeq protein FASTA shards plus a viewer-friendly file/shard index. The full sequence data is stored as 1,725 `.fasta.zst` shards and the per-record metadata JSONL files are very large, so the default Dataset Viewer table indexes repository files instead of expanding all 459,415,871 protein records.

Use the original `sequences/.../shard-*.fasta.zst` files for complete FASTA records. Use the default Parquet table for Dataset Viewer previews, source discovery, file sizes, record counts, and download patterns.

## Splits

The split is deterministic by file ID: `sha256(file_id) % 10`. Bucket `0` is `test`; buckets `1` through `9` are `train`.

| Split | Rows |
|---|---:|
| train | 4,676 |
| test | 502 |
| total | 5,178 |

## Source Statistics

| Field | Value |
|---|---:|
| Source FASTA files | 1,725 |
| RefSeq protein records | 459,415,871 |
| Residues | 179,203,453,293 |
| Sequence shards | 1,725 |
| Compressed sequence shard bytes | 78,108,688,857 |
| Metadata JSONL bytes | 158,533,041,909 |

## Usage

```bash
pip install datasets
```

Load the shard index:

```python
from datasets import load_dataset

ds = load_dataset("LiteFold/NCBI")
print(ds)
print(ds["train"][0])
```

Load one split:

```python
from datasets import load_dataset

train = load_dataset("LiteFold/NCBI", split="train")
test = load_dataset("LiteFold/NCBI", split="test")
```

List sequence shards:

```python
from datasets import load_dataset

index = load_dataset("LiteFold/NCBI", split="train")
shards = index.filter(lambda row: row["is_sequence_shard"])
print(shards[0]["path"])
```

Find a source FASTA and its files:

```python
from datasets import load_dataset

index = load_dataset("LiteFold/NCBI", split="train")
rows = index.filter(lambda row: row["source_file"] == "sequence/ncbi_refseq/release_complete/complete.1486.protein.faa.gz")
for row in rows:
    print(row["role"], row["path"], row["size_bytes"])
```

Download all sequence shards:

```bash
hf download LiteFold/NCBI --repo-type dataset \
  --include 'sequences/*/shard-*.fasta.zst' \
  --local-dir ./ncbi_refseq_protein
```

Download one source shard:

```bash
hf download LiteFold/NCBI --repo-type dataset \
  --include 'sequences/sequence_ncbi_refseq_release_complete_complete.1486.protein.faa.gz/shard-*.fasta.zst' \
  --local-dir ./ncbi_refseq_protein
```

Stream a downloaded shard with Python:

```python
from pathlib import Path
import zstandard as zstd

shard = next(Path("./ncbi_refseq_protein").rglob("shard-*.fasta.zst"))
dctx = zstd.ZstdDecompressor()
with shard.open("rb") as f, dctx.stream_reader(f) as reader:
    print(reader.read(1024).decode("utf-8", errors="replace"))
```

## Columns

| Column | Description |
|---|---|
| `file_id` | Stable row ID, equal to the repository path. |
| `repo_id` | Hugging Face dataset repository. |
| `source_sha` | Source repository commit used to build the index. |
| `dataset_id` | Source dataset identifier from the manifest. |
| `source_slug` | Source slug from the original pipeline manifest. |
| `source_file` | Original source FASTA file path. |
| `path` | File path in the repository. |
| `role` | File role, such as `sequence_shard`, `metadata_records`, or `source_manifest`. |
| `shard_index` | Numeric shard index for sequence shards. |
| `size_bytes` | File size in bytes. |
| `compression` | Compression format, when applicable. |
| `records_in_source` | Protein record count for the source FASTA file. |
| `residues_in_source` | Residue count for the source FASTA file. |
| `shards_in_source` | Shard count for the source FASTA file. |
| `records_total` | Total protein record count from the aggregate manifest. |
| `residues_total` | Total residue count from the aggregate manifest. |
| `total_shards` | Total sequence shard count. |
| `is_sequence_shard` | Whether the row points to a FASTA shard. |
| `is_metadata_records` | Whether the row points to a per-record metadata JSONL. |
| `download_pattern` | Recommended path or glob for downloading. |
| `access_note` | Note describing the index scope. |
| `split_bucket` | Deterministic split bucket from `sha256(file_id) % 10`. |

## Preparation

The normalization script used to create the Parquet files is included at `scripts/prepare_ncbi_dataset.py`.