UniRef90 Shard Index

This dataset contains the original UniRef90 FASTA shards plus a viewer-friendly file/shard index. The full sequence data is stored as 189 .fasta.zst shards and the per-record metadata JSONL is very large, so the default Dataset Viewer table indexes repository files instead of expanding all 188,848,220 sequence 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, 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	175
test	19
total	194

Source Statistics

Field	Value
UniRef90 records	188,848,220
Residues	66,359,825,357
Sequence shards	189
Compressed sequence shard bytes	41,498,595,315
Metadata JSONL bytes	66,419,395,026

Usage

pip install datasets

Load the shard index:

from datasets import load_dataset

ds = load_dataset("LiteFold/UniRef90")
print(ds)
print(ds["train"][0])

Load one split:

from datasets import load_dataset

train = load_dataset("LiteFold/UniRef90", split="train")

List sequence shards:

from datasets import load_dataset

index = load_dataset("LiteFold/UniRef90", split="train")
shards = index.filter(lambda row: row["is_sequence_shard"])
print(shards[0]["path"])

Download sequence shards:

hf download LiteFold/UniRef90 --repo-type dataset \
  --include 'sequences/sequence_uniref90_uniref90.fasta.gz/shard-*.fasta.zst' \
  --local-dir ./uniref90

Stream a downloaded shard with Python:

from pathlib import Path
import zstandard as zstd

shard = next(Path("./uniref90").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.
source_slug	Source slug from the original pipeline manifest.
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_total	Total UniRef90 records from the manifest.
residues_total	Total residue count from the 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 the 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_uniref90_dataset.py.