Datasets:

LiteFold
/

UniRef50

license: cc-by-4.0
pretty_name: UniRef50
size_categories:
  - 10M<n<100M
task_categories:
  - other
language:
  - en
tags:
  - biology
  - proteins
  - sequences
  - fasta
  - uniref
  - clustering

UniRef50

Normalized FASTA shards of UniRef50 cluster representative sequences (50% sequence identity threshold).

Processed and uploaded by the MegaData post-download pipeline (internal repo). Original source: https://www.uniprot.org/help/uniref.

Statistics


Source files	1
Shards	61
Compressed shard bytes	10.74 GiB (11,527,890,402)
Records (per-source manifest sum)	60,315,044
Residues (per-source manifest sum)	17,282,055,793
Aggregate manifest `total_records`	60315044

Layout

.
├── _MANIFEST.json                      # aggregate manifest written by the pipeline
├── manifests/<source_slug>.json        # per-source manifest (records, residues, shards)
├── metadata/<source_slug>.records.jsonl  # per-record provenance
└── sequences/<source_slug>/shard-NNNNNN.fasta.zst

<source_slug> corresponds 1:1 with an upstream source archive; e.g. sequence_uniprotkb_uniprot_sprot.fasta.gz.

Loading

Stream every shard of one source (replace <source_slug> with the directory of interest under sequences/):

hf download LiteFold/UniRef50 --repo-type dataset \
  --include 'sequences/<source_slug>/shard-*.fasta.zst' \
  --local-dir ./uniref50
zstd -dc ./uniref50/sequences/<source_slug>/shard-*.fasta.zst | head

Programmatic streaming with zstandard:

from huggingface_hub import snapshot_download
from pathlib import Path
import zstandard as zstd

local = snapshot_download(
    repo_id="LiteFold/UniRef50",
    repo_type="dataset",
    allow_patterns=["sequences/*/shard-*.fasta.zst"],
)

dctx = zstd.ZstdDecompressor()
for shard in sorted(Path(local).rglob("shard-*.fasta.zst")):
    with shard.open("rb") as f, dctx.stream_reader(f) as reader:
        buf = b""
        while chunk := reader.read(1 << 20):
            buf += chunk
            *lines, buf = buf.split(b"\n")
            for line in lines:
                ...  # naive splitter; swap in your FASTA parser

License

CC BY 4.0 (UniProt Consortium).

Citation

Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH; UniProt Consortium. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 31(6):926-32, 2015.

Provenance

Built from the local manifest entry uniref50 of manifests/atlas_download_plan.json. Pipeline source: megadata-post normalize --dataset uniref50.